notebooks-finetuning-models/02_finetune_v1_malaysian_debertav2_base.ipynb

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      "metadata": {
        "id": "view-in-github",
        "colab_type": "text"
      },
      "source": [
        "<a href=\"https://colab.research.google.com/github/wanadzhar913/aitinkerers-hackathon-supa-team-werecooked/blob/master/notebooks-finetuning-models/02_finetune_v1_malaysian_debertav2_base.ipynb\" target=\"_parent\"><img src=\"https://colab.research.google.com/assets/colab-badge.svg\" alt=\"Open In Colab\"/></a>"
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "In this notebook we finetune [mesolitica/malaysian-debertav2-base](https://huggingface.co/mesolitica/malaysian-debertav2-base). DeBERTa (Decoding-enhanced BERT with disentangled attention) for a Natural language inference (NLI) task. In our case, NLI is the task of determining whether a \"hypothesis\" is true (*entailment*) or false (*contradiction*) given a question-statement pair. DeBERTa is selected due to its [SOTA performance in comparison to other models like BERT and RoBERTAa](https://wandb.ai/akshayuppal12/DeBERTa/reports/The-Next-Generation-of-Transformers-Leaving-BERT-Behind-With-DeBERTa--VmlldzoyNDM2NTk2#:~:text=What%20we%20do%20see%3A%20for,accuracy%20for%20the%20validation%20set.).\n",
        "\n",
        "Overall, solely using the [Boolq-Malay](https://huggingface.co/datasets/wanadzhar913/boolq-malay) dataset (comprised of both Malay and English versions of the original [Boolq](https://huggingface.co/datasets/google/boolq) dataset), we obtain the follwing results:\n",
        "\n",
        "- **No. of Epochs:** 10\n",
        "- **Accuracy:** 66%  \n",
        "- **F1-Score:** 65%  \n",
        "- **Recall:** 65%  \n",
        "- **Precision:** 66%  \n",
        "\n",
        "The model can be found here: https://huggingface.co/wanadzhar913/malaysian-debertav2-finetune-on-boolq\n",
        "\n",
        "In the future, we can do the following to garner better results:\n",
        "- Increase the `gradient_accumulation_steps` to deal with the small GPU constraints or increase the `batch_size` if we've access to a larger GPU. The reasoning is mainly to avoid [Out of Memory Errors (OOM)](https://discuss.huggingface.co/t/batch-size-vs-gradient-accumulation/5260).\n",
        "- Given more compute resources, we can also increase our `patience` variable and train for more than 10 epochs.\n",
        "\n"
      ],
      "metadata": {
        "id": "ONhm9h7qEGmu"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "!pip install datasets huggingface_hub -q"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "pIRiPkfZyq1m",
        "outputId": "9ea0b6ec-16e0-4c6a-9614-319decba66b1"
      },
      "execution_count": 1,
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          "name": "stdout",
          "text": [
            "\u001b[?25l   \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m0.0/480.6 kB\u001b[0m \u001b[31m?\u001b[0m eta \u001b[36m-:--:--\u001b[0m\r\u001b[2K   \u001b[91m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[90m╺\u001b[0m \u001b[32m471.0/480.6 kB\u001b[0m \u001b[31m14.3 MB/s\u001b[0m eta \u001b[36m0:00:01\u001b[0m\r\u001b[2K   \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m480.6/480.6 kB\u001b[0m \u001b[31m10.3 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n",
            "\u001b[?25h\u001b[?25l   \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m0.0/116.3 kB\u001b[0m \u001b[31m?\u001b[0m eta \u001b[36m-:--:--\u001b[0m\r\u001b[2K   \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m116.3/116.3 kB\u001b[0m \u001b[31m10.5 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n",
            "\u001b[?25h\u001b[?25l   \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m0.0/179.3 kB\u001b[0m \u001b[31m?\u001b[0m eta \u001b[36m-:--:--\u001b[0m\r\u001b[2K   \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m179.3/179.3 kB\u001b[0m \u001b[31m18.3 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n",
            "\u001b[?25h\u001b[?25l   \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m0.0/134.8 kB\u001b[0m \u001b[31m?\u001b[0m eta \u001b[36m-:--:--\u001b[0m\r\u001b[2K   \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m134.8/134.8 kB\u001b[0m \u001b[31m11.6 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n",
            "\u001b[?25h\u001b[?25l   \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m0.0/194.1 kB\u001b[0m \u001b[31m?\u001b[0m eta \u001b[36m-:--:--\u001b[0m\r\u001b[2K   \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m194.1/194.1 kB\u001b[0m \u001b[31m15.5 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n",
            "\u001b[?25h\u001b[31mERROR: pip's dependency resolver does not currently take into account all the packages that are installed. This behaviour is the source of the following dependency conflicts.\n",
            "gcsfs 2024.10.0 requires fsspec==2024.10.0, but you have fsspec 2024.9.0 which is incompatible.\u001b[0m\u001b[31m\n",
            "\u001b[0m"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# import transformers\n",
        "# import datasets\n",
        "# import huggingface_hub\n",
        "# import sklearn\n",
        "# import torch\n",
        "# import numpy as np\n",
        "\n",
        "# print(f'transformers version: {transformers.__version__}')\n",
        "# print(f'datasets version: {datasets.__version__}')\n",
        "# print(f'huggingface_hub version: {huggingface_hub.__version__}')\n",
        "# print(f'scikit-learn version: {sklearn.__version__}')\n",
        "# print(f'torch version: {torch.__version__}')\n",
        "# print(f'numpy version: {np.__version__}')"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "ITYhcFbZKuDO",
        "outputId": "d07eee90-0d54-4d3e-e9a7-9ca46fcb4e93"
      },
      "execution_count": 6,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "transformers version: 4.46.2\n",
            "datasets version: 3.1.0\n",
            "huggingface_hub version: 0.26.2\n",
            "scikit-learn version: 1.5.2\n",
            "torch version: 2.5.1+cu121\n",
            "numpy version: 1.26.4\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "ZtlC7jvQxx2Q"
      },
      "outputs": [],
      "source": [
        "import os\n",
        "import json\n",
        "import random\n",
        "\n",
        "from tqdm import tqdm\n",
        "from datasets import load_dataset\n",
        "from huggingface_hub import create_repo, notebook_login\n",
        "from sklearn.metrics import classification_report\n",
        "\n",
        "import torch\n",
        "import numpy as np\n",
        "from transformers import AutoTokenizer, AutoConfig, pipeline, \\\n",
        "                         DebertaV2ForSequenceClassification"
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "notebook_login()"
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        "outputId": "7487fbae-d0bb-42d9-b0cb-345eded7d34e"
      },
      "execution_count": null,
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        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
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      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "### 1.0 Load dataset"
      ],
      "metadata": {
        "id": "sQrlEcUv7qYb"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "ds_train = load_dataset(\"wanadzhar913/boolq-malay\", split=\"train\").shuffle(seed=42)\n",
        "ds_test = load_dataset(\"wanadzhar913/boolq-malay\", split=\"validation\").shuffle(seed=42)"
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      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "/usr/local/lib/python3.10/dist-packages/huggingface_hub/utils/_token.py:89: UserWarning: \n",
            "The secret `HF_TOKEN` does not exist in your Colab secrets.\n",
            "To authenticate with the Hugging Face Hub, create a token in your settings tab (https://huggingface.co/settings/tokens), set it as secret in your Google Colab and restart your session.\n",
            "You will be able to reuse this secret in all of your notebooks.\n",
            "Please note that authentication is recommended but still optional to access public models or datasets.\n",
            "  warnings.warn(\n"
          ]
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "boolq-english-train.jsonl:   0%|          | 0.00/6.50M [00:00<?, ?B/s]"
            ],
            "application/vnd.jupyter.widget-view+json": {
              "version_major": 2,
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              "model_id": "c10a3b5c58aa477cad41910e516377d6"
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          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
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              "boolq-malay-train-fixed.jsonl:   0%|          | 0.00/7.15M [00:00<?, ?B/s]"
            ],
            "application/vnd.jupyter.widget-view+json": {
              "version_major": 2,
              "version_minor": 0,
              "model_id": "b87b1d58230c41378768207aeea1853e"
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          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
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              "boolq-english-val.jsonl:   0%|          | 0.00/2.25M [00:00<?, ?B/s]"
            ],
            "application/vnd.jupyter.widget-view+json": {
              "version_major": 2,
              "version_minor": 0,
              "model_id": "42ef807b852148838fe80c7fbdf10092"
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          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "boolq-malay-val-fixed.jsonl:   0%|          | 0.00/2.47M [00:00<?, ?B/s]"
            ],
            "application/vnd.jupyter.widget-view+json": {
              "version_major": 2,
              "version_minor": 0,
              "model_id": "ddec115d70074372ba6826144ca0b199"
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          },
          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "Generating train split:   0%|          | 0/18851 [00:00<?, ? examples/s]"
            ],
            "application/vnd.jupyter.widget-view+json": {
              "version_major": 2,
              "version_minor": 0,
              "model_id": "e3c979e0f35d46a9846e594c193965bc"
            }
          },
          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "Generating validation split:   0%|          | 0/6540 [00:00<?, ? examples/s]"
            ],
            "application/vnd.jupyter.widget-view+json": {
              "version_major": 2,
              "version_minor": 0,
              "model_id": "b91e38c1449244a6ad3124a54144a345"
            }
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "train_X = ds_train.map(lambda x: {'input': (x['question'], x['passage'])}) \\\n",
        "                  .remove_columns(['question', 'answer', 'passage', 'language', 'split'])\n",
        "test_X = ds_test.map(lambda x: {'input': (x['question'], x['passage'])}) \\\n",
        "                .remove_columns(['question', 'answer', 'passage', 'language', 'split'])\n",
        "\n",
        "train_Y = ds_train.select_columns([\"answer\"])\n",
        "test_Y = ds_test.select_columns([\"answer\"])"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 81,
          "referenced_widgets": [
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            "4cc9670c66194e65a220a2b36ff86406",
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            "e07ffed1ffb74cea80b82250ab5a82d6",
            "a0d91b6b333642998e82eca791cdfcca",
            "c085b19618fd4617be92ce62b3ec4e9b",
            "b5403aeccf5247049abbdae7dc3c9181"
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        },
        "id": "52GKAkO10h1-",
        "outputId": "81ddca26-cbc6-43a7-ceef-ff0b9d72c679"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "Map:   0%|          | 0/18851 [00:00<?, ? examples/s]"
            ],
            "application/vnd.jupyter.widget-view+json": {
              "version_major": 2,
              "version_minor": 0,
              "model_id": "31e050b17aae4ab3ab59b847ca808c09"
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          },
          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "Map:   0%|          | 0/6540 [00:00<?, ? examples/s]"
            ],
            "application/vnd.jupyter.widget-view+json": {
              "version_major": 2,
              "version_minor": 0,
              "model_id": "a316e06abf9a48ffa73c0abe2e64e3c6"
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          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "train_X[53:55]['input']"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "xm0EnuWd1nKv",
        "outputId": "b175b0fb-e0f5-47d3-90a9-0f38c75c7442"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "[['is colby jack the same as monterey jack',\n",
              "  'Colby-Jack, or Cojack, is a cheese produced from a mixture of Colby and Monterey Jack cheeses. It is generally sold in a full-moon or a half-moon shape when it is still young and mild in flavor. The cheese has a semi-hard texture. The flavor of Colby-Jack is mild to mellow.'],\n",
              " ['adakah tidak dapat mengeja merupakan satu bentuk disleksia',\n",
              "  'Disleksia, juga dikenali sebagai gangguan membaca, dicirikan oleh kesukaran dalam membaca walaupun mempunyai kecerdasan yang normal. Orang yang berbeza terjejas pada tahap yang berbeza-beza. Masalah mungkin termasuk kesukaran dalam mengeja perkataan, membaca dengan cepat, menulis perkataan, \"mengeluarkan bunyi\" perkataan dalam kepala, menyebut perkataan semasa membaca dengan kuat dan memahami apa yang dibaca. Selalunya kesukaran ini pertama kali diperhatikan di sekolah. Apabila seseorang yang sebelum ini boleh membaca kehilangan kebolehan mereka, ia dikenali sebagai aleksia. Kesukaran ini adalah tidak disengajakan dan orang yang mempunyai gangguan ini mempunyai keinginan yang normal untuk belajar.']]"
            ]
          },
          "metadata": {},
          "execution_count": 9
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "train_Y[53:55]['answer']"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "AEGJm0Pi1nBS",
        "outputId": "125eedc8-1fc2-40e0-f903-392128da1733"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "[0, 1]"
            ]
          },
          "metadata": {},
          "execution_count": 7
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "test_X['input'][53:60]"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "miO0xQJ4skLb",
        "outputId": "c3a8e217-d5e3-4a90-8720-741194ad109b"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "[['adakah balkans sebahagian daripada empayar uthmaniyyah',\n",
              "  'Sebahagian besar Balkan berada di bawah pemerintahan Uthmaniyyah sepanjang tempoh moden awal. Pemerintahan Uthmaniyyah berlangsung lama, dari abad ke-14 sehingga awal abad ke-20 di beberapa wilayah. Empayar Uthmaniyyah adalah pelbagai dari segi agama, bahasa dan etnik, dan, pada masa-masa tertentu, merupakan tempat yang lebih toleran untuk amalan agama berbanding dengan bahagian lain di dunia. Kumpulan-kumpulan yang berbeza dalam empayar diatur mengikut garis pengakuan, dalam sistem yang dipanggil sistem Millet. Di kalangan penganut Kristian Ortodoks dalam empayar (Rum Millet), identiti bersama dibentuk berdasarkan rasa masa yang dikongsi yang ditentukan oleh kalendar gerejawi, hari-hari santo dan perayaan.'],\n",
              " ['has every mountain in the world been climbed',\n",
              "  \"An unclimbed mountain is a mountain peak that has yet to be climbed to the top. Determining which unclimbed peak is highest is often a matter of controversy. In some parts of the world, surveying and mapping are still unreliable, and there are no comprehensive records of the routes of explorers, mountaineers and local inhabitants. In some cases, even modern ascents by larger parties have been poorly documented and, with no universally recognized listing, the best that can be achieved in determining the world's highest unclimbed peaks is somewhat speculative. Most sources indicate that Gangkhar Puensum (7,570 metres (24,840 ft)) in Bhutan or on the Bhutan-China border is the tallest mountain in the world that has yet to be fully summited. Gangkhar Puensum has been off limits to climbers since 1994 when Bhutan prohibited all mountaineering above 6,000 m (20,000 ft) due to spiritual/religious beliefs.\"],\n",
              " ['bolehkah sisihan piawai lebih besar daripada 1',\n",
              "  'Bagi fulmar jantan, pengiraan yang serupa memberikan sisihan piawai sampel sebanyak 894.37, hampir dua kali ganda lebih besar daripada sisihan piawai bagi betina. Graf menunjukkan data kadar metabolik, purata (titik merah), dan sisihan piawai (garis merah) untuk betina dan jantan.'],\n",
              " ['adakah sesiapa telah memenangi lebih daripada satu pingat kehormatan kongres',\n",
              "  'Sehingga kini, jumlah maksimum Pingat Kehormatan yang diperoleh oleh mana-mana anggota perkhidmatan adalah dua. Individu terakhir yang masih hidup yang dianugerahkan dua Pingat Kehormatan adalah John J. Kelly pada 3 Okt 1918; individu terakhir yang menerima dua Pingat Kehormatan untuk dua tindakan yang berbeza adalah Smedley Butler, pada tahun 1914 dan 1915.'],\n",
              " ['is there such thing as a no carb diet',\n",
              "  'A no-carbohydrate diet (no-carb diet, zero carb diet) excludes dietary consumption of all carbohydrates (including dietary fiber) and suggests fat as the main source of energy with sufficient protein. A no-carbohydrate diet may be ketogenic, which means it causes the body to go into a state of ketosis, converting dietary fat and body fat into ketone bodies which are used to fuel parts of the body that do not oxidize fat for energy, especially the brain. Some bodily organs and parts of the brain still require glucose, which is tightly regulated by the liver and adequately supplied by gluconeogenesis or by converting glycerol from the breakdown of triglycerides. A no-carbohydrate diet may use mainly animal source foods and may include a high saturated fat intake, though this is not prescriptive of the diet, which, by definition, only restricts carbohydrate intake.'],\n",
              " ['adakah oasis of the seas kapal pelayaran terbesar',\n",
              "  'Kelas Oasis adalah kelas kapal pelayaran Royal Caribbean International yang merupakan kapal penumpang terbesar di dunia. Dua kapal pertama dalam kelas ini, Oasis of the Seas dan Allure of the Seas, diserahkan masing-masing pada tahun 2009 dan 2010 oleh STX Europe Turku Shipyard, Finland. Sebuah kapal kelas Oasis ketiga, Harmony of the Seas, diserahkan pada tahun 2016 yang dibina oleh STX France, dan sebuah kapal keempat, MS Symphony of the Seas, telah disiapkan pada bulan Mac 2018. Satu kapal tambahan yang belum dinamakan sedang dalam pembinaan dan dijadualkan untuk diserahkan pada tahun 2021. Dua kapal pertama dalam kelas ini, Oasis of the Seas dan Allure of the Seas, sedikit lebih kecil daripada kapal ketiga, Harmony of the Seas, sementara Symphony of the Seas adalah kapal pelayaran terbesar di dunia. Kapal kelima, yang dijadualkan siap pada musim bunga 2021, dirancang untuk lebih besar daripada Symphony of the Seas.'],\n",
              " ['are there in n outs outside of california',\n",
              "  \"In-N-Out Burger is an American regional chain of fast food restaurants with locations primarily in the American Southwest and Pacific coast. It was founded in Baldwin Park, California in 1948 by Harry Snyder and Esther Snyder. The chain is currently headquartered in Irvine, California and has slowly expanded outside Southern California into the rest of California, as well as into Arizona, Nevada, Utah, Texas, and Oregon. The current owner is Lynsi Snyder, the Snyders' only grandchild.\"]]"
            ]
          },
          "metadata": {},
          "execution_count": 6
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "test_Y[53:60]['answer']"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "GsmRHMxLsdpS",
        "outputId": "e65c1d5e-92a4-496b-dd45-04be1e2e7cbe"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "[1, 0, 1, 1, 1, 0, 1]"
            ]
          },
          "metadata": {},
          "execution_count": 9
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "### 2.0 Load and set model configs"
      ],
      "metadata": {
        "id": "TVsq_VCe7uu4"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "config = AutoConfig.from_pretrained('mesolitica/malaysian-debertav2-base')"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 49,
          "referenced_widgets": [
            "4af6b428ff384479920ff7389c3adf2d",
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        },
        "id": "Qva60aHA2Bgl",
        "outputId": "c0dd6f59-ca5c-4b03-8431-cdf218f738d4"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "config.json:   0%|          | 0.00/892 [00:00<?, ?B/s]"
            ],
            "application/vnd.jupyter.widget-view+json": {
              "version_major": 2,
              "version_minor": 0,
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          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "model = DebertaV2ForSequenceClassification.from_pretrained('mesolitica/malaysian-debertav2-base', config = config)\n",
        "_ = model.cuda()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 104,
          "referenced_widgets": [
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        },
        "id": "Zct-zbTo2Jh2",
        "outputId": "b213343c-bd9b-4c38-cfb2-18fb6c76d417"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "model.safetensors:   0%|          | 0.00/228M [00:00<?, ?B/s]"
            ],
            "application/vnd.jupyter.widget-view+json": {
              "version_major": 2,
              "version_minor": 0,
              "model_id": "31373e91db07404981fee8d3f09e7eab"
            }
          },
          "metadata": {}
        },
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "Some weights of DebertaV2ForSequenceClassification were not initialized from the model checkpoint at mesolitica/malaysian-debertav2-base and are newly initialized: ['classifier.bias', 'classifier.weight', 'pooler.dense.bias', 'pooler.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"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "tokenizer = AutoTokenizer.from_pretrained('mesolitica/malaysian-debertav2-base')"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 113,
          "referenced_widgets": [
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        },
        "id": "vdwrPlGp2f4U",
        "outputId": "32b2b28c-01a5-4728-e2ba-8ae082f3f796"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "tokenizer_config.json:   0%|          | 0.00/1.22k [00:00<?, ?B/s]"
            ],
            "application/vnd.jupyter.widget-view+json": {
              "version_major": 2,
              "version_minor": 0,
              "model_id": "8f869cf2723e4a88bcd8c312867b8167"
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          },
          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "tokenizer.json:   0%|          | 0.00/1.34M [00:00<?, ?B/s]"
            ],
            "application/vnd.jupyter.widget-view+json": {
              "version_major": 2,
              "version_minor": 0,
              "model_id": "4418cfa410564f7d9e1189d061355509"
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          },
          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "special_tokens_map.json:   0%|          | 0.00/778 [00:00<?, ?B/s]"
            ],
            "application/vnd.jupyter.widget-view+json": {
              "version_major": 2,
              "version_minor": 0,
              "model_id": "8e9b25d6955846b0b2324c1d99fd070e"
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          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "!nvidia-smi"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "qmyFkADg3GwK",
        "outputId": "d8422541-b6be-4f4c-8da4-fee89592fa2c"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Sat Oct 19 04:57:00 2024       \n",
            "+---------------------------------------------------------------------------------------+\n",
            "| NVIDIA-SMI 535.104.05             Driver Version: 535.104.05   CUDA Version: 12.2     |\n",
            "|-----------------------------------------+----------------------+----------------------+\n",
            "| GPU  Name                 Persistence-M | Bus-Id        Disp.A | Volatile Uncorr. ECC |\n",
            "| Fan  Temp   Perf          Pwr:Usage/Cap |         Memory-Usage | GPU-Util  Compute M. |\n",
            "|                                         |                      |               MIG M. |\n",
            "|=========================================+======================+======================|\n",
            "|   0  NVIDIA A100-SXM4-40GB          Off | 00000000:00:04.0 Off |                    0 |\n",
            "| N/A   34C    P0              53W / 400W |    899MiB / 40960MiB |      0%      Default |\n",
            "|                                         |                      |             Disabled |\n",
            "+-----------------------------------------+----------------------+----------------------+\n",
            "                                                                                         \n",
            "+---------------------------------------------------------------------------------------+\n",
            "| Processes:                                                                            |\n",
            "|  GPU   GI   CI        PID   Type   Process name                            GPU Memory |\n",
            "|        ID   ID                                                             Usage      |\n",
            "|=======================================================================================|\n",
            "+---------------------------------------------------------------------------------------+\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "### 3.0 Train/finetune model"
      ],
      "metadata": {
        "id": "gq_rozim71up"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "trainable_parameters = [param for param in model.parameters() if param.requires_grad]\n",
        "trainer = torch.optim.AdamW(trainable_parameters, lr = 1e-5, eps=1e-08, betas=(0.9,0.999))\n",
        "\n",
        "# Add a ReduceLROnPlateau scheduler\n",
        "scheduler = torch.optim.lr_scheduler.ReduceLROnPlateau(trainer, mode='max', patience=2, factor=0.5, verbose=True)"
      ],
      "metadata": {
        "id": "NerGvEeh2jaD"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": [
        "We see the model **overfitting towards the end** but we're out of compute resources so this'll have to do. In the future, I'd reduce the `patient` variable."
      ],
      "metadata": {
        "id": "oW7WtAlGiMNn"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "batch_size = 8 # small batch size to avoid OutOfMemory errors :(\n",
        "epoch = 10\n",
        "\n",
        "best_dev_acc = -np.inf\n",
        "patient = 3\n",
        "current_patient = 0\n",
        "\n",
        "for e in range(epoch):\n",
        "    pbar = tqdm(range(0, len(train_X), batch_size))\n",
        "    losses = []\n",
        "    for i in pbar:\n",
        "        trainer.zero_grad()\n",
        "        x = train_X[i: i + batch_size]\n",
        "        y = np.array(train_Y['answer'][i: i + batch_size])\n",
        "\n",
        "        padded = tokenizer(x['input'], padding = 'longest', return_tensors = 'pt')\n",
        "\n",
        "        padded['labels'] = torch.from_numpy(y)\n",
        "        for k in padded.keys():\n",
        "            padded[k] = padded[k].cuda()\n",
        "\n",
        "        padded.pop('token_type_ids', None)\n",
        "\n",
        "        loss, pred = model(**padded, return_dict = False)\n",
        "        loss.backward()\n",
        "\n",
        "        grad_norm = torch.nn.utils.clip_grad_norm_(trainable_parameters, 5.0)\n",
        "        trainer.step()\n",
        "        losses.append(float(loss))\n",
        "\n",
        "    dev_predicted = []\n",
        "    for i in range(0, len(test_X), batch_size):\n",
        "        x = test_X[i: i + batch_size]\n",
        "        y = np.array(test_Y['answer'][i: i + batch_size])\n",
        "\n",
        "        padded = tokenizer(x['input'], padding = 'longest', return_tensors = 'pt')\n",
        "        padded['labels'] = torch.from_numpy(y)\n",
        "        for k in padded.keys():\n",
        "            padded[k] = padded[k].cuda()\n",
        "\n",
        "        loss, pred = model(**padded, return_dict = False)\n",
        "        dev_predicted.append((pred.argmax(axis = 1).detach().cpu().numpy() == y).mean())\n",
        "\n",
        "    dev_predicted = np.mean(dev_predicted)\n",
        "\n",
        "    # Call scheduler.step() with the validation accuracy\n",
        "    scheduler.step(dev_predicted)\n",
        "\n",
        "    print(f'epoch: {e}, loss: {np.mean(losses)}, dev_predicted: {dev_predicted}')\n",
        "\n",
        "    if dev_predicted >= best_dev_acc:\n",
        "        best_dev_acc = dev_predicted\n",
        "        current_patient = 0\n",
        "        model.save_pretrained('malaysian-debertav2-finetune-on-boolq')\n",
        "    else:\n",
        "        current_patient += 1\n",
        "\n",
        "    if current_patient >= patient:\n",
        "        break\n",
        "\n",
        "    if e == epoch - 1:\n",
        "        print(f'Final dev accuracy: {best_dev_acc}')\n",
        "        model.save_pretrained('malaysian-debertav2-finetune-on-boolq')"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "n21bv5O32mQD",
        "outputId": "24077289-325b-4092-b055-0344f84f04a0"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "100%|██████████| 2357/2357 [09:24<00:00,  4.18it/s]\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "epoch: 0, loss: 0.6468807298296531, dev_predicted: 0.6612163814180929\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "100%|██████████| 2357/2357 [09:24<00:00,  4.18it/s]\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "epoch: 1, loss: 0.49742775551791435, dev_predicted: 0.6523533007334963\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "100%|██████████| 2357/2357 [09:25<00:00,  4.17it/s]\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "epoch: 2, loss: 0.25414175960548635, dev_predicted: 0.6392114914425427\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "100%|██████████| 2357/2357 [09:25<00:00,  4.17it/s]\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "epoch: 3, loss: 0.1292089269987623, dev_predicted: 0.6667176039119804\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "100%|██████████| 2357/2357 [09:25<00:00,  4.17it/s]\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "epoch: 4, loss: 0.09572274779887335, dev_predicted: 0.6731356968215159\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "100%|██████████| 2357/2357 [09:26<00:00,  4.16it/s]\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "epoch: 5, loss: 0.07463395613032514, dev_predicted: 0.6752750611246944\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "100%|██████████| 2357/2357 [09:26<00:00,  4.16it/s]\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "epoch: 6, loss: 0.07290740855837491, dev_predicted: 0.6763447432762836\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "100%|██████████| 2357/2357 [09:26<00:00,  4.16it/s]\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "epoch: 7, loss: 0.05595923417469376, dev_predicted: 0.6648838630806846\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "100%|██████████| 2357/2357 [09:26<00:00,  4.16it/s]\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "epoch: 8, loss: 0.053639279538313, dev_predicted: 0.648380195599022\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "100%|██████████| 2357/2357 [09:26<00:00,  4.16it/s]\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "epoch: 9, loss: 0.04436893333102321, dev_predicted: 0.6580073349633252\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "### 4.0 Run benchmarks"
      ],
      "metadata": {
        "id": "L2rtvZUQ-nYt"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "label2id = {'contradiction' : 0, 'entailment' : 1}\n",
        "id2label = {0 : 'contradiction', 1 : 'entailment'}"
      ],
      "metadata": {
        "id": "SLVEpNlKEBs3"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "config.num_labels = 2\n",
        "config.vocab = ['contradiction', 'entailment']"
      ],
      "metadata": {
        "id": "kyAtP4rVEHO_"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "model.config.label2id = label2id\n",
        "model.config.id2label = id2label"
      ],
      "metadata": {
        "id": "In8lqyfVEL34"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "batch_size = 10\n",
        "epoch = 100\n",
        "\n",
        "real_Y = []\n",
        "for i in tqdm(range(0, len(test_X), batch_size)):\n",
        "    x = test_X[i: i + batch_size]\n",
        "    y = np.array(test_Y['answer'][i: i + batch_size])\n",
        "\n",
        "    padded = tokenizer(x['input'], padding = 'longest', return_tensors = 'pt')\n",
        "    padded['labels'] = torch.from_numpy(y)\n",
        "    for k in padded.keys():\n",
        "        padded[k] = padded[k].cuda()\n",
        "    padded.pop('token_type_ids', None)\n",
        "\n",
        "    loss, pred = model(**padded, return_dict=False)\n",
        "    real_Y.extend(pred.argmax(axis = 1).detach().cpu().numpy().tolist())"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "FrWiB3AysgKt",
        "outputId": "9cc657aa-de85-4648-a703-4be217b0fe6f"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "100%|██████████| 654/654 [01:04<00:00, 10.19it/s]\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "padded"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "PxsluYvuhNf8",
        "outputId": "823c5cce-cc46-4a41-a72f-a27855c270c2"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "{'input_ids': tensor([[  974, 20557,  3407,  ...,     0,     0,     0],\n",
              "        [ 9259, 12227, 22431,  ...,     0,     0,     0],\n",
              "        [  498,  2395,  1624,  ...,     0,     0,     0],\n",
              "        ...,\n",
              "        [  807,  4044,   856,  ...,     0,     0,     0],\n",
              "        [ 9259,  5937,  2892,  ...,    16,   612,    17],\n",
              "        [ 1221,   384,   418,  ...,     0,     0,     0]], device='cuda:0'), 'attention_mask': tensor([[1, 1, 1,  ..., 0, 0, 0],\n",
              "        [1, 1, 1,  ..., 0, 0, 0],\n",
              "        [1, 1, 1,  ..., 0, 0, 0],\n",
              "        ...,\n",
              "        [1, 1, 1,  ..., 0, 0, 0],\n",
              "        [1, 1, 1,  ..., 1, 1, 1],\n",
              "        [1, 1, 1,  ..., 0, 0, 0]], device='cuda:0'), 'labels': tensor([1, 0, 1, 1, 1, 1, 1, 1, 1, 1], device='cuda:0')}"
            ]
          },
          "metadata": {},
          "execution_count": 19
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "pred.argmax(axis = 1).detach().cpu().numpy()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "8FsjwHhehE1v",
        "outputId": "f77d4ea1-8c49-4dba-f7fd-fe97b31548c5"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "array([1, 0, 1, 1, 1, 0, 1, 0, 1, 1])"
            ]
          },
          "metadata": {},
          "execution_count": 20
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "pred"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "5J-dFfuehIqt",
        "outputId": "d5b1bc9d-68f2-4ff4-9280-4da60cf453c9"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "tensor([[-4.6697,  4.4405],\n",
              "        [ 1.1710, -1.7389],\n",
              "        [-3.0831,  2.9493],\n",
              "        [-4.5986,  4.2763],\n",
              "        [-4.1859,  3.9571],\n",
              "        [ 0.3446, -0.9267],\n",
              "        [-3.0447,  2.7374],\n",
              "        [ 3.3037, -4.0914],\n",
              "        [-3.3666,  3.1480],\n",
              "        [-4.4459,  4.2598]], device='cuda:0', grad_fn=<AddmmBackward0>)"
            ]
          },
          "metadata": {},
          "execution_count": 21
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "print(\n",
        "    classification_report(\n",
        "        real_Y, test_Y['answer'], # target_names = config.vocab,\n",
        "        digits = 5\n",
        "    )\n",
        ")"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "RF7K59B86l65",
        "outputId": "02040d70-82fc-4e48-dd0b-e169c97e8ac1"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "              precision    recall  f1-score   support\n",
            "\n",
            "           0    0.66047   0.53910   0.59364      3031\n",
            "           1    0.65642   0.76062   0.70469      3509\n",
            "\n",
            "    accuracy                        0.65795      6540\n",
            "   macro avg    0.65844   0.64986   0.64916      6540\n",
            "weighted avg    0.65830   0.65795   0.65322      6540\n",
            "\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "pipe = pipeline(\n",
        "    \"text-classification\",\n",
        "    tokenizer = tokenizer,\n",
        "    model=model,\n",
        "    padding=True\n",
        ")"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "WnF1etBo-mnl",
        "outputId": "5334a8c5-f9bf-4cfc-ecd5-74c8c7767dee"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "Hardware accelerator e.g. GPU is available in the environment, but no `device` argument is passed to the `Pipeline` object. Model will be on CPU.\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "article = \"\"\"\n",
        "KUALA LUMPUR: Kerajaan bersetuju untuk menaikkan kadar gaji minimum daripada RM1,500 sebulan kepada RM1,700, berkuat kuasa 1 Februari 2025.\n",
        "Perdana Menteri Datuk Seri Anwar Ibrahim sewaktu membentangkan Belanjawan 2025 Malaysia MADANI di Dewan Rakyat pada Jumaat berkata, penstrukturan ekonomi hanya dianggap berjaya apabila rakyat meraih gaji dan upah yang bermakna untuk menjalani hidup dengan lebih selesa.\n",
        "\"\"\""
      ],
      "metadata": {
        "id": "StQSC8Ur-_K-"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "pipe([('Betul ke kerajaan naikkan gaji minimum?', article)])"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "r-XctIsa-9YK",
        "outputId": "d8ed7d09-174b-4a55-f15c-bc0e91073a8a"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "[{'label': 'entailment', 'score': 0.8098661303520203}]"
            ]
          },
          "metadata": {},
          "execution_count": 28
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "pipe([('Did the government top up minimum wage?', article)])"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "pPee9H4Vhpzb",
        "outputId": "e7b72570-bec2-4ecb-c148-23012df3d387"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "[{'label': 'entailment', 'score': 0.9928961396217346}]"
            ]
          },
          "metadata": {},
          "execution_count": 30
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "pipe([('Government naikkan gaji minimum', article)])"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "jmd_QEyRhyRT",
        "outputId": "dc76e8a1-afcb-4a01-e5af-91946dca3ff4"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "[{'label': 'entailment', 'score': 0.7880232334136963}]"
            ]
          },
          "metadata": {},
          "execution_count": 32
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "### 5.0 Push model to Huggingface 🤗"
      ],
      "metadata": {
        "id": "BxaJIGif_sG7"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "create_repo(\"wanadzhar913/malaysian-debertav2-finetune-on-boolq\", repo_type=\"model\")"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 69
        },
        "id": "sLV2-V6U_HEc",
        "outputId": "c78bbbb2-1f61-487c-d753-b1088687de08"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "RepoUrl('https://huggingface.co/wanadzhar913/malaysian-debertav2-finetune-on-boolq', endpoint='https://huggingface.co', repo_type='model', repo_id='wanadzhar913/malaysian-debertav2-finetune-on-boolq')"
            ],
            "application/vnd.google.colaboratory.intrinsic+json": {
              "type": "string"
            }
          },
          "metadata": {},
          "execution_count": 15
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "model.push_to_hub('wanadzhar913/malaysian-debertav2-finetune-on-boolq', safe_serialization = True)"
      ],
      "metadata": {
        "id": "ECSx-Dop_OPi",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 118,
          "referenced_widgets": [
            "326759c3dab94a03859c85f385f19aa7",
            "e0861d59fcea46209f6dbdd14d301e9d",
            "425953907edb409f99e7f4ff3fb8c425",
            "b90a197001ac4420b70beea1694c3f2f",
            "47c316d535ca4f60bb584b5e138f9e96",
            "5d806a85b0ec4f509e5c61751e35968e",
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              "README.md:   0%|          | 0.00/5.17k [00:00<?, ?B/s]"
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