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  },
  "cells": [
    {
      "cell_type": "code",
      "source": [
        "from google.colab import drive\n",
        "drive.mount(\"/content/drive\")"
      ],
      "metadata": {
        "id": "PFNDnd0VuW5q"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "Jd63Y5vP3SmW",
        "outputId": "277bf8f2-15cc-4650-f26c-b11547adcd12"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "\u001b[2K     \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m251.6/251.6 kB\u001b[0m \u001b[31m5.4 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n",
            "\u001b[2K     \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m309.4/309.4 kB\u001b[0m \u001b[31m7.8 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n",
            "\u001b[2K     \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m21.3/21.3 MB\u001b[0m \u001b[31m57.0 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n",
            "\u001b[2K     \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m84.1/84.1 kB\u001b[0m \u001b[31m3.3 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n",
            "\u001b[2K     \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m547.8/547.8 kB\u001b[0m \u001b[31m9.0 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n",
            "\u001b[2K     \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m116.3/116.3 kB\u001b[0m \u001b[31m14.7 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n",
            "\u001b[2K     \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m194.1/194.1 kB\u001b[0m \u001b[31m12.9 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n",
            "\u001b[2K     \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m134.8/134.8 kB\u001b[0m \u001b[31m9.5 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n",
            "\u001b[2K     \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m40.8/40.8 MB\u001b[0m \u001b[31m12.5 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n",
            "\u001b[2K     \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m64.9/64.9 kB\u001b[0m \u001b[31m7.4 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",
            "cudf-cu12 24.4.1 requires pyarrow<15.0.0a0,>=14.0.1, but you have pyarrow 16.1.0 which is incompatible.\n",
            "google-colab 1.0.0 requires requests==2.31.0, but you have requests 2.32.3 which is incompatible.\n",
            "ibis-framework 8.0.0 requires pyarrow<16,>=2, but you have pyarrow 16.1.0 which is incompatible.\u001b[0m\u001b[31m\n",
            "\u001b[0m"
          ]
        }
      ],
      "source": [
        "# installing packages\n",
        "!pip install -q transformers\n",
        "!pip install -q peft\n",
        "!pip install -q evaluate"
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# loading dataset\n",
        "from datasets import load_dataset\n",
        "\n",
        "dataset = load_dataset(\"persiannlp/parsinlu_sentiment\")"
      ],
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        "id": "EiQp4dQq3aKD",
        "outputId": "6fa38afc-1250-4cb2-cc60-7048fd2169bf"
      },
      "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": [
              "Downloading data:   0%|          | 0.00/436k [00:00<?, ?B/s]"
            ],
            "application/vnd.jupyter.widget-view+json": {
              "version_major": 2,
              "version_minor": 0,
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          },
          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "Downloading data:   0%|          | 0.00/45.0k [00:00<?, ?B/s]"
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          },
          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
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              "Downloading data:   0%|          | 0.00/38.2k [00:00<?, ?B/s]"
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            "application/vnd.jupyter.widget-view+json": {
              "version_major": 2,
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        },
        {
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            "application/vnd.jupyter.widget-view+json": {
              "version_major": 2,
              "version_minor": 0,
              "model_id": "a2ab7e0567cb4299a0fdf5c85d7832f1"
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          },
          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
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              "Downloading data:   0%|          | 0.00/21.8k [00:00<?, ?B/s]"
            ],
            "application/vnd.jupyter.widget-view+json": {
              "version_major": 2,
              "version_minor": 0,
              "model_id": "30b13505b24a4b32a7b6c7ccc1f34dde"
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          },
          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "Generating train split:   0%|          | 0/13617 [00:00<?, ? examples/s]"
            ],
            "application/vnd.jupyter.widget-view+json": {
              "version_major": 2,
              "version_minor": 0,
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          },
          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
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            ],
            "application/vnd.jupyter.widget-view+json": {
              "version_major": 2,
              "version_minor": 0,
              "model_id": "0849ef5ad25f4297ade81c744588aa53"
            }
          },
          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "Generating test_movies split:   0%|          | 0/816 [00:00<?, ? examples/s]"
            ],
            "application/vnd.jupyter.widget-view+json": {
              "version_major": 2,
              "version_minor": 0,
              "model_id": "823045620f1f4f85a8ea54e0e464efdb"
            }
          },
          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
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            ],
            "application/vnd.jupyter.widget-view+json": {
              "version_major": 2,
              "version_minor": 0,
              "model_id": "3798a55e46104270810a5378dffc450a"
            }
          },
          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "Generating validation_movies split:   0%|          | 0/360 [00:00<?, ? examples/s]"
            ],
            "application/vnd.jupyter.widget-view+json": {
              "version_major": 2,
              "version_minor": 0,
              "model_id": "092890d2ab8c47ba88bcf70e2032dbe7"
            }
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "dataset"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "nj9fSlCn4Ogo",
        "outputId": "85a0665b-da89-4db5-914e-541dc3136c44"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "DatasetDict({\n",
              "    train: Dataset({\n",
              "        features: ['review', 'review_id', 'example_id', 'excel_id', 'question', 'category', 'aspect', 'label', 'guid'],\n",
              "        num_rows: 13617\n",
              "    })\n",
              "    test_food: Dataset({\n",
              "        features: ['review', 'review_id', 'example_id', 'excel_id', 'question', 'category', 'aspect', 'label', 'guid'],\n",
              "        num_rows: 1344\n",
              "    })\n",
              "    test_movies: Dataset({\n",
              "        features: ['review', 'review_id', 'example_id', 'excel_id', 'question', 'category', 'aspect', 'label', 'guid'],\n",
              "        num_rows: 816\n",
              "    })\n",
              "    validation_food: Dataset({\n",
              "        features: ['review', 'review_id', 'example_id', 'excel_id', 'question', 'category', 'aspect', 'label', 'guid'],\n",
              "        num_rows: 1330\n",
              "    })\n",
              "    validation_movies: Dataset({\n",
              "        features: ['review', 'review_id', 'example_id', 'excel_id', 'question', 'category', 'aspect', 'label', 'guid'],\n",
              "        num_rows: 360\n",
              "    })\n",
              "})"
            ]
          },
          "metadata": {},
          "execution_count": 6
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "dataset['train'][1]"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "ENS9yVvonWe8",
        "outputId": "be2eafde-71b3-447f-f598-d7794e77bde9"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "{'review': 'دوستان حتما دقت کنید درقسمت فیله تکه های سبز رنگ داشت که نشان دهنده انتیبیوتیک هایی هست که به مرغ ها میدن و اون تکه سمیه. بیشترشم استخونه من راضی نبودم و دیگه ازین مارو خرید نمیکنم',\n",
              " 'review_id': '1',\n",
              " 'example_id': '2',\n",
              " 'excel_id': 'food_1744',\n",
              " 'question': 'نظر شما در مورد قیمت و ارزش خرید این گوشت مرغ چیست؟',\n",
              " 'category': 'گوشت مرغ',\n",
              " 'aspect': 'ارزش خرید',\n",
              " 'label': '-3',\n",
              " 'guid': 'food-train-r1-e2'}"
            ]
          },
          "metadata": {},
          "execution_count": 7
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "dataset['test_food'][1]"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "nKbmtop3ne1D",
        "outputId": "9bf9aa6e-856b-4430-f248-732f6bdc9b7f"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "{'review': 'مدت هاست که از محصول همین برند استفاده می کنم. کیفیت کاملا قابل قبولی داره\\u200c و قیمتش مناسبه.',\n",
              " 'review_id': '1726',\n",
              " 'example_id': '2',\n",
              " 'excel_id': 'food_197',\n",
              " 'question': 'نظر شما در مورد قیمت و ارزش خرید این قهوه چیست؟',\n",
              " 'category': 'قهوه',\n",
              " 'aspect': 'ارزش خرید',\n",
              " 'label': '1',\n",
              " 'guid': 'food-test-r1726-e2'}"
            ]
          },
          "metadata": {},
          "execution_count": 8
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "dataset['test_movies'][1]"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "bU4LtXLUnk2_",
        "outputId": "a247780a-3767-4b0f-9c68-a96717f9ec98"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "{'review': 'یک فیلم با موضوع جدید و عجیب و شاید برای خیلیها غیرقابل باور ! دیدنش برام خیلی جذاب بود البته برای خیلیها هم نبود، اینکه در آخر فیلم از خودت میپرسی خب که چی؟! یکم حس سردرگمی بهت میداد و نمیزاشت همزادپنداری کنی با شخصیت اول فیلم! ولی در کل پیشنهاد میشود.',\n",
              " 'review_id': '405',\n",
              " 'example_id': '2',\n",
              " 'excel_id': 'movie_152',\n",
              " 'question': 'نظر شما در مورد داستان، فیلمنامه، دیالوگ ها و موضوع فیلم  درساژ چیست؟',\n",
              " 'category': ' درساژ',\n",
              " 'aspect': 'داستان',\n",
              " 'label': '3',\n",
              " 'guid': 'movie-test-r405-e2'}"
            ]
          },
          "metadata": {},
          "execution_count": 9
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "dataset['validation_movies'][1]"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "qph7zwsBntAu",
        "outputId": "094f50aa-27ff-471c-8608-0c707d957dd2"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "{'review': 'فیلمی که با تمام وجود روحتون رو آزار می ده. نمی دونم چرا موضوعات ناراحت کننده، نشون دادن بدبختی و زجر کشیدن آدمها، جدیدا اینقدر جذاب شده!!! دیدن این فیلم رو اصلا توصیه نمی کنم!',\n",
              " 'review_id': '360',\n",
              " 'example_id': '2',\n",
              " 'excel_id': 'movie_338',\n",
              " 'question': 'نظر شما در مورد داستان، فیلمنامه، دیالوگ ها و موضوع فیلم  همه چیز برای فروش چیست؟',\n",
              " 'category': ' همه چیز برای فروش',\n",
              " 'aspect': 'داستان',\n",
              " 'label': '-1',\n",
              " 'guid': 'movie-dev-r360-e2'}"
            ]
          },
          "metadata": {},
          "execution_count": 10
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "dataset['validation_food'][1]"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "b5tD-6y0n0K8",
        "outputId": "bfcfe733-a980-4e7c-f580-622c08c64f34"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "{'review': 'من خیلی دنبال این طمع گشتم تا تو دیجی پیداش کردم . من از طعمش خوشم اومد البته ممکنه کسی هم خوشش نیاد',\n",
              " 'review_id': '1536',\n",
              " 'example_id': '2',\n",
              " 'excel_id': 'food_425',\n",
              " 'question': 'نظر شما در مورد قیمت و ارزش خرید این آدامس و خوشبو کننده\\u200cی دهان چیست؟',\n",
              " 'category': 'آدامس و خوشبو کننده\\u200cی دهان',\n",
              " 'aspect': 'ارزش خرید',\n",
              " 'label': '-3',\n",
              " 'guid': 'food-dev-r1536-e2'}"
            ]
          },
          "metadata": {},
          "execution_count": 11
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "   # this is dataset labels\n",
        "\n",
        "    # '-3': 'no sentiment expressed',\n",
        "    # '-2': 'very negative',\n",
        "    # '-1': 'negative',\n",
        "    # '0': 'neutral',\n",
        "    # '1': 'positive',\n",
        "    # '2': 'very positive',\n",
        "    # '3': 'mixed',\n"
      ],
      "metadata": {
        "id": "baOTK12Qm4g8"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "# our model need int label so wee need to map the label to int, since our model only has 3 classes we only need label -1,0,1 from dataset\n",
        "label_mapping = {\n",
        "    '-1': 0,  # negative\n",
        "    '0': 1,   # neutral\n",
        "    '1': 2    # positive\n",
        "}\n",
        "\n",
        "def filter_function(example):\n",
        "    return example['label'] in label_mapping\n",
        "\n",
        "# Filter dataset\n",
        "filtered_train_dataset = dataset['train'].filter(filter_function)\n",
        "filtered_validation_dataset = dataset['validation_movies'].filter(filter_function)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 81,
          "referenced_widgets": [
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        },
        "id": "8MIuqkSFtohR",
        "outputId": "57da1f31-1c51-47cc-a642-c16b815816b5"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "Filter:   0%|          | 0/13617 [00:00<?, ? examples/s]"
            ],
            "application/vnd.jupyter.widget-view+json": {
              "version_major": 2,
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          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "Filter:   0%|          | 0/360 [00:00<?, ? examples/s]"
            ],
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        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "set(filtered_train_dataset['label'])"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "5L4tFpmBvXeY",
        "outputId": "b05fb88a-0d19-4c98-d133-a7085e3f595d"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "{'-1', '0', '1'}"
            ]
          },
          "metadata": {},
          "execution_count": 13
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "from transformers import AutoTokenizer\n",
        "# downloading model and tokenizer\n",
        "\n",
        "model_name_or_path = \"nimaafshar/parsbert-fa-sentiment-twitter\"\n",
        "tokenizer = AutoTokenizer.from_pretrained(model_name_or_path)\n",
        "\n",
        "# here I tokenize review which is a string text and I have int lables\n",
        "def tokenize_function(examples):\n",
        "    tokenized = tokenizer(examples['review'], padding=\"max_length\", truncation=True, max_length=128)\n",
        "    tokenized['labels'] = label_mapping[examples['label']]  # Convert labels to integers using the mapping\n",
        "    return tokenized\n",
        "\n",
        "tokenized_train_dataset = filtered_train_dataset.map(tokenize_function, batched=False, remove_columns=['review', 'review_id', 'example_id', 'excel_id', 'question', 'category', 'aspect', 'label', 'guid'])\n",
        "tokenized_validation_dataset = filtered_validation_dataset.map(tokenize_function, batched=False, remove_columns=['review', 'review_id', 'example_id', 'excel_id', 'question', 'category', 'aspect', 'label', 'guid'])"
      ],
      "metadata": {
        "colab": {
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      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "tokenizer_config.json:   0%|          | 0.00/62.0 [00:00<?, ?B/s]"
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          "name": "stderr",
          "text": [
            "/usr/local/lib/python3.10/dist-packages/huggingface_hub/file_download.py:1132: FutureWarning: `resume_download` is deprecated and will be removed in version 1.0.0. Downloads always resume when possible. If you want to force a new download, use `force_download=True`.\n",
            "  warnings.warn(\n"
          ]
        },
        {
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          "data": {
            "text/plain": [
              "config.json:   0%|          | 0.00/770 [00:00<?, ?B/s]"
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              "vocab.txt:   0%|          | 0.00/1.20M [00:00<?, ?B/s]"
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              "model_id": "8b27e3a7935a43579fa7a305f2cc78a2"
            }
          },
          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "special_tokens_map.json:   0%|          | 0.00/112 [00:00<?, ?B/s]"
            ],
            "application/vnd.jupyter.widget-view+json": {
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              "model_id": "7ca74cb2568c4f71bc0dc02ec3f299ff"
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          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "Map:   0%|          | 0/2046 [00:00<?, ? examples/s]"
            ],
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              "version_major": 2,
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              "model_id": "89cf4f2a5ef54848b1a77030113fad4a"
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          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "Map:   0%|          | 0/34 [00:00<?, ? examples/s]"
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            "application/vnd.jupyter.widget-view+json": {
              "version_major": 2,
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              "model_id": "b88cdc73962e481a95031772d91e76b0"
            }
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# checking my data after tokenizing\n",
        "tokenized_train_dataset"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "u3Fc70VlKHEd",
        "outputId": "517d4a75-a665-4988-f545-8cd3801e83d2"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "Dataset({\n",
              "    features: ['input_ids', 'token_type_ids', 'attention_mask', 'labels'],\n",
              "    num_rows: 2046\n",
              "})"
            ]
          },
          "metadata": {},
          "execution_count": 15
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "tokenized_validation_dataset"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "YMJRSxa_qu1H",
        "outputId": "6597302f-8d09-4fd4-d78e-fa597ae43387"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "Dataset({\n",
              "    features: ['input_ids', 'token_type_ids', 'attention_mask', 'labels'],\n",
              "    num_rows: 34\n",
              "})"
            ]
          },
          "metadata": {},
          "execution_count": 16
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "tokenized_train_dataset[0]"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "collapsed": true,
        "id": "CH-LAR3GpKv1",
        "outputId": "7be42739-13ed-4537-d923-27a4cb542928"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
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              " 'labels': 1}"
            ]
          },
          "metadata": {},
          "execution_count": 17
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "from peft import LoraConfig, TaskType\n",
        "# LORA configuration object using Hugging Face’s Efficient Fine-Tuning (PEFT) parameters.\n",
        "# The task_Type parameter specifies the task type for which the model will undergo fine-tuning.\n",
        "lora_config = LoraConfig(\n",
        "    task_type=TaskType.SEQ_CLS, r=1, lora_alpha=1, lora_dropout=0.1\n",
        ")"
      ],
      "metadata": {
        "id": "oX_Sgbho35cy"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "from transformers import BertForSequenceClassification\n",
        "# Load a pre-trained BERT model for sequence classification with 3 labels\n",
        "model = BertForSequenceClassification.from_pretrained(\n",
        "    model_name_or_path,\n",
        "    num_labels=3,\n",
        "    from_tf = True\n",
        ")"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 122,
          "referenced_widgets": [
            "01830c6162204699a236070dd7b99566",
            "c54b03c98a054bc5ad3b15e4513d3fb4",
            "ec0afc2dd78746e4a5f7a402bc1d7598",
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            "ac0eb047e1d04fdfbd14c66958a2e3b8",
            "62a58e9bad2f4e9aa293b9541b5fa1a6"
          ]
        },
        "id": "oS2KFLn2358a",
        "outputId": "58a3d980-c7f0-4548-c91a-a44602e4f4ec"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "tf_model.h5:   0%|          | 0.00/652M [00:00<?, ?B/s]"
            ],
            "application/vnd.jupyter.widget-view+json": {
              "version_major": 2,
              "version_minor": 0,
              "model_id": "01830c6162204699a236070dd7b99566"
            }
          },
          "metadata": {}
        },
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "All TF 2.0 model weights were used when initializing BertForSequenceClassification.\n",
            "\n",
            "All the weights of BertForSequenceClassification were initialized from the TF 2.0 model.\n",
            "If your task is similar to the task the model of the checkpoint was trained on, you can already use BertForSequenceClassification for predictions without further training.\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "model"
      ],
      "metadata": {
        "id": "A2fOhNjyqEab"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "from peft import get_peft_model\n",
        "# Apply the PEFT (Parameter-Efficient Fine-Tuning) model with LoRA configuration to the pre-trained model\n",
        "model = get_peft_model(model, lora_config)"
      ],
      "metadata": {
        "id": "iLIW1Mzy38E1"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "import numpy as np\n",
        "import evaluate\n",
        "\n",
        "metric = evaluate.load(\"accuracy\")"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 49,
          "referenced_widgets": [
            "2872f6507fed4412a19437fa2b97a5a8",
            "8401bfb59b5f4f6d852fe6e88da8b2b7",
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            "751fd8cac1f541c1b6ba6baf06b5e72c",
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            "42757c8a375e401f86ca72712c09b792",
            "6b255f9fe9544384ac1931813ac6aedb"
          ]
        },
        "id": "kQA3H1D73-mz",
        "outputId": "44080d39-e9f4-4550-ad18-dfad38b4f2c9"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "Downloading builder script:   0%|          | 0.00/4.20k [00:00<?, ?B/s]"
            ],
            "application/vnd.jupyter.widget-view+json": {
              "version_major": 2,
              "version_minor": 0,
              "model_id": "2872f6507fed4412a19437fa2b97a5a8"
            }
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# Define a function to compute evaluation metrics\n",
        "def compute_metrics(eval_pred):\n",
        "    logits, labels = eval_pred\n",
        "    predictions = np.argmax(logits, axis=-1)\n",
        "    return metric.compute(predictions=predictions, references=labels)"
      ],
      "metadata": {
        "id": "Vv1wKy6b4BXn"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "from transformers import TrainingArguments, Trainer\n",
        "# Set up training arguments, including output directory, evaluation strategy, and number of epochs\n",
        "training_args = TrainingArguments(output_dir=\"/content/drive/MyDrive/anewstart/karlancer/sentiment/test_trainer1\", evaluation_strategy=\"epoch\",\n",
        "                                 num_train_epochs=25,)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "cI4TjkGu4Dpg",
        "outputId": "be98a00c-2ee1-42fd-c423-09167442f829"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "/usr/local/lib/python3.10/dist-packages/transformers/training_args.py:1474: FutureWarning: `evaluation_strategy` is deprecated and will be removed in version 4.46 of 🤗 Transformers. Use `eval_strategy` instead\n",
            "  warnings.warn(\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "tokenized_train_dataset['labels'][0]"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "Qr4qymtksHHO",
        "outputId": "ec703127-58e8-4b9b-a203-9e4d5b207a5e"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "1"
            ]
          },
          "metadata": {},
          "execution_count": 25
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "set(tokenized_train_dataset['labels'])"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "Qf1wvjI0wWeT",
        "outputId": "ed7904ea-a9cc-4663-a5b8-6ccbe71da834"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "{0, 1, 2}"
            ]
          },
          "metadata": {},
          "execution_count": 26
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# # Enable detailed error reporting\n",
        "# import os\n",
        "# os.environ['CUDA_LAUNCH_BLOCKING'] = '1'"
      ],
      "metadata": {
        "id": "6rKlBDdfwzTj"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "!nvidia-smi"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "M903u8LVxVTM",
        "outputId": "842585ff-ef8b-4e6e-83ce-574f3b0dd8d2"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Mon Jul  1 08:20:32 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  Tesla T4                       Off | 00000000:00:04.0 Off |                    0 |\n",
            "| N/A   43C    P0              27W /  70W |   2165MiB / 15360MiB |      0%      Default |\n",
            "|                                         |                      |                  N/A |\n",
            "+-----------------------------------------+----------------------+----------------------+\n",
            "                                                                                         \n",
            "+---------------------------------------------------------------------------------------+\n",
            "| Processes:                                                                            |\n",
            "|  GPU   GI   CI        PID   Type   Process name                            GPU Memory |\n",
            "|        ID   ID                                                             Usage      |\n",
            "|=======================================================================================|\n",
            "+---------------------------------------------------------------------------------------+\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# Print the labels to ensure they are correct\n",
        "print(\"Labels in the tokenized train dataset:\", [example['labels'] for example in tokenized_train_dataset])\n",
        "print(\"Labels in the tokenized validation dataset:\", [example['labels'] for example in tokenized_validation_dataset])"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "CyIgnJgAx6od",
        "outputId": "515c4fa0-daea-488b-ffee-67575c5f954e"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Labels in the tokenized train dataset: [1, 2, 2, 2, 2, 2, 2, 2, 0, 2, 2, 0, 0, 0, 0, 0, 2, 0, 0, 2, 2, 2, 2, 2, 0, 2, 2, 0, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 0, 2, 0, 2, 2, 2, 2, 0, 0, 0, 0, 2, 2, 0, 0, 2, 2, 0, 2, 2, 2, 2, 0, 2, 2, 2, 2, 2, 2, 0, 0, 2, 2, 2, 2, 2, 0, 2, 2, 0, 2, 2, 2, 0, 0, 0, 0, 2, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 2, 0, 2, 2, 2, 2, 2, 0, 0, 0, 1, 0, 2, 2, 2, 2, 2, 1, 2, 0, 0, 2, 0, 2, 2, 0, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 2, 0, 0, 0, 2, 0, 0, 2, 0, 2, 2, 2, 2, 0, 0, 0, 2, 2, 2, 2, 2, 0, 0, 2, 0, 2, 0, 0, 0, 0, 0, 0, 2, 0, 0, 2, 0, 0, 2, 2, 2, 2, 0, 2, 2, 2, 2, 0, 0, 0, 2, 2, 0, 0, 2, 2, 0, 0, 2, 2, 2, 1, 0, 0, 0, 0, 2, 2, 0, 0, 0, 2, 2, 2, 2, 2, 2, 2, 0, 2, 2, 0, 0, 0, 0, 2, 2, 2, 2, 2, 0, 0, 2, 2, 2, 0, 0, 0, 0, 0, 0, 0, 2, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 2, 2, 2, 2, 0, 0, 0, 2, 0, 0, 0, 0, 0, 0, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 0, 0, 0, 0, 0, 0, 0, 2, 2, 0, 0, 2, 2, 0, 0, 0, 2, 2, 0, 2, 2, 2, 0, 2, 2, 2, 2, 1, 2, 2, 2, 2, 2, 0, 0, 0, 2, 0, 2, 2, 2, 2, 2, 1, 2, 2, 0, 0, 2, 2, 2, 2, 0, 0, 0, 0, 2, 2, 2, 2, 0, 0, 0, 0, 0, 0, 0, 0, 0, 2, 2, 2, 0, 2, 2, 0, 2, 2, 2, 2, 2, 0, 0, 0, 0, 0, 0, 0, 0, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 0, 0, 2, 0, 0, 0, 2, 0, 0, 2, 2, 2, 2, 0, 0, 0, 0, 2, 2, 2, 2, 2, 2, 2, 2, 2, 0, 0, 2, 0, 0, 2, 2, 2, 2, 2, 2, 0, 0, 2, 0, 0, 0, 2, 0, 0, 2, 0, 2, 0, 2, 2, 2, 0, 0, 2, 2, 2, 0, 2, 2, 2, 2, 0, 0, 0, 0, 0, 0, 0, 2, 2, 0, 0, 2, 2, 2, 0, 0, 2, 0, 0, 2, 2, 2, 2, 0, 2, 0, 2, 0, 0, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 0, 0, 2, 2, 2, 2, 2, 0, 0, 0, 0, 0, 2, 2, 2, 2, 2, 2, 2, 2, 0, 0, 0, 0, 0, 2, 2, 2, 2, 2, 2, 2, 2, 0, 2, 2, 2, 0, 0, 0, 0, 0, 0, 0, 0, 0, 2, 2, 0, 0, 0, 0, 0, 2, 0, 0, 2, 2, 0, 0, 0, 0, 0, 2, 0, 0, 0, 0, 0, 0, 0, 0, 0, 2, 2, 2, 0, 0, 2, 2, 0, 0, 0, 0, 0, 2, 0, 2, 0, 0, 0, 2, 2, 2, 2, 2, 2, 2, 0, 0, 1, 2, 2, 1, 2, 0, 2, 0, 0, 0, 0, 0, 1, 0, 2, 2, 2, 0, 0, 0, 0, 2, 2, 2, 0, 0, 2, 0, 2, 0, 2, 0, 0, 0, 2, 2, 0, 0, 2, 2, 2, 2, 2, 2, 0, 0, 2, 0, 0, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 0, 0, 0, 2, 2, 0, 0, 2, 2, 0, 2, 2, 0, 0, 2, 2, 2, 2, 2, 0, 2, 2, 2, 2, 0, 0, 0, 2, 0, 0, 0, 0, 0, 0, 0, 2, 0, 2, 2, 0, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 0, 2, 2, 0, 0, 2, 2, 0, 1, 0, 0, 2, 2, 2, 2, 0, 0, 0, 0, 0, 0, 0, 2, 2, 2, 2, 0, 0, 2, 2, 2, 2, 2, 0, 0, 0, 2, 2, 2, 0, 0, 2, 2, 0, 0, 2, 2, 0, 0, 2, 0, 2, 0, 0, 0, 0, 0, 2, 2, 2, 2, 2, 2, 2, 2, 2, 0, 0, 0, 0, 0, 2, 2, 2, 2, 0, 2, 0, 0, 0, 2, 2, 0, 2, 0, 0, 0, 0, 0, 2, 0, 0, 2, 0, 2, 0, 0, 0, 2, 2, 2, 0, 0, 2, 2, 2, 0, 0, 2, 2, 2, 0, 0, 2, 2, 2, 0, 0, 2, 0, 0, 0, 2, 2, 2, 2, 2, 0, 0, 0, 0, 0, 2, 2, 2, 0, 0, 0, 2, 0, 0, 0, 2, 2, 2, 2, 2, 2, 2, 2, 0, 0, 1, 0, 2, 2, 2, 0, 2, 0, 0, 0, 0, 0, 0, 2, 2, 2, 2, 2, 0, 2, 2, 2, 0, 0, 2, 2, 2, 0, 2, 2, 0, 2, 2, 2, 2, 2, 2, 0, 2, 2, 0, 0, 2, 2, 2, 2, 0, 0, 1, 2, 2, 2, 0, 0, 2, 2, 2, 0, 2, 0, 2, 0, 2, 2, 0, 0, 0, 2, 2, 0, 0, 0, 1, 2, 2, 2, 2, 2, 0, 2, 0, 0, 2, 2, 2, 2, 0, 2, 0, 2, 0, 0, 0, 2, 2, 0, 0, 0, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 1, 2, 0, 2, 2, 0, 2, 2, 2, 0, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 0, 2, 2, 0, 0, 0, 0, 0, 2, 2, 1, 2, 2, 2, 2, 2, 1, 1, 2, 2, 2, 2, 2, 2, 1, 2, 0, 0, 0, 0, 2, 2, 2, 1, 0, 0, 2, 2, 2, 0, 0, 2, 2, 0, 0, 2, 0, 0, 0, 2, 2, 2, 0, 0, 2, 0, 2, 2, 2, 2, 2, 0, 0, 2, 2, 0, 0, 0, 0, 0, 2, 2, 0, 2, 0, 0, 0, 0, 0, 0, 2, 2, 2, 0, 0, 1, 2, 0, 2, 2, 0, 0, 2, 2, 0, 2, 2, 2, 0, 0, 2, 2, 2, 2, 2, 2, 2, 0, 2, 2, 0, 2, 0, 2, 2, 2, 2, 0, 2, 2, 2, 0, 2, 2, 2, 2, 2, 2, 0, 0, 2, 2, 2, 0, 0, 2, 2, 2, 2, 0, 0, 2, 2, 0, 0, 0, 0, 0, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 0, 2, 2, 0, 0, 2, 2, 2, 0, 0, 0, 2, 2, 2, 2, 2, 2, 0, 2, 2, 0, 2, 2, 0, 0, 0, 0, 2, 2, 2, 0, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 0, 2, 2, 2, 0, 0, 0, 0, 2, 0, 0, 2, 1, 2, 2, 0, 2, 2, 2, 2, 2, 0, 2, 0, 0, 2, 2, 2, 2, 2, 2, 0, 2, 1, 2, 2, 2, 2, 2, 2, 2, 2, 0, 0, 0, 0, 2, 2, 0, 0, 0, 2, 2, 2, 0, 2, 0, 2, 0, 0, 2, 2, 2, 2, 0, 0, 2, 2, 0, 0, 2, 0, 2, 2, 2, 2, 0, 0, 0, 2, 2, 2, 0, 0, 2, 0, 2, 2, 0, 0, 2, 2, 2, 0, 0, 2, 2, 2, 2, 2, 0, 0, 0, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 0, 0, 0, 0, 0, 0, 2, 2, 2, 0, 2, 2, 2, 0, 2, 2, 2, 2, 0, 1, 0, 0, 0, 2, 2, 0, 0, 0, 2, 2, 2, 2, 0, 0, 2, 2, 0, 0, 0, 0, 2, 2, 0, 0, 0, 0, 0, 0, 2, 2, 2, 2, 0, 0, 0, 0, 0, 0, 2, 2, 2, 0, 0, 0, 2, 2, 2, 2, 1, 2, 2, 2, 2, 2, 2, 0, 0, 2, 2, 2, 2, 2, 0, 2, 2, 0, 0, 0, 2, 2, 2, 2, 0, 2, 2, 2, 0, 2, 1, 2, 2, 2, 0, 0, 0, 0, 0, 0, 2, 2, 2, 0, 2, 0, 2, 2, 0, 0, 2, 2, 0, 0, 0, 0, 0, 2, 0, 0, 2, 0, 0, 0, 2, 2, 0, 0, 0, 2, 2, 2, 2, 2, 0, 0, 2, 0, 0, 0, 2, 2, 0, 0, 2, 2, 2, 0, 2, 0, 0, 2, 0, 0, 0, 0, 0, 2, 2, 2, 0, 2, 2, 2, 0, 0, 2, 2, 2, 2, 0, 0, 0, 0, 0, 2, 2, 0, 0, 2, 2, 0, 2, 2, 2, 0, 0, 2, 2, 2, 2, 0, 0, 0, 0, 2, 2, 2, 2, 2, 2, 0, 2, 0, 0, 2, 0, 0, 0, 2, 0, 0, 0, 2, 2, 2, 2, 2, 0, 2, 2, 0, 1, 2, 2, 2, 2, 0, 0, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 0, 0, 0, 2, 2, 2, 0, 0, 2, 2, 2, 0, 0, 0, 0, 0, 2, 2, 0, 0, 0, 0, 2, 2, 0, 0, 2, 2, 2, 2, 2, 2, 0, 2, 0, 0, 2, 2, 0, 1, 2, 0, 0, 0, 0, 0, 2, 2, 0, 0, 2, 2, 0, 2, 2, 1, 0, 2, 2, 2, 2, 2, 2, 2, 2, 0, 0, 2, 2, 0, 2, 2, 2, 2, 2, 0, 0, 2, 2, 0, 0, 0, 0, 0, 2, 2, 2, 2, 0, 0, 0, 0, 0, 1, 2, 0, 0, 1, 2, 2, 0, 0, 2, 2, 2, 0, 0, 2, 2, 0, 0, 0, 2, 0, 0, 2, 2, 2, 2, 2, 2, 0, 2, 0, 0, 0, 0, 2, 0, 0, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 0, 1, 0, 0, 0, 0, 1, 2, 2, 2, 2, 2, 2, 2, 2, 0, 0, 0, 1, 0, 2, 2, 2, 0, 2, 2, 2, 2, 0, 0, 0, 2, 2, 2, 0, 0, 2, 0, 0, 0, 1, 0, 0, 0, 0, 0, 2, 1, 2, 2, 0, 0, 0, 1, 2, 2, 2, 2, 2, 2, 2, 0, 2, 2, 1, 0, 2, 2, 2, 2, 2, 0, 0, 1, 2, 2, 2, 1, 2, 1, 0, 0, 2, 2, 2, 2, 0, 2, 2, 2, 2, 2, 2, 2, 2, 0, 2, 2, 2, 1, 2, 2, 2, 2, 0, 0, 0, 0, 0, 0, 0, 0, 0, 2, 0, 2, 2, 2, 1, 0, 0, 2, 1, 0, 2, 0, 2, 2, 0, 0, 2, 2, 2, 0, 0, 0, 0, 0, 0, 0, 0, 2, 2, 0, 2, 0, 0, 0, 1, 2, 2, 2, 0, 2, 2, 0, 0, 0, 0, 0, 0, 0, 2, 0, 0, 2, 2, 2, 2, 2, 2, 2, 0, 2, 2, 0, 2, 2, 0, 0, 2, 2, 2, 2, 0, 1, 2, 0, 0, 0, 2, 2, 0, 0, 0, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 0, 0, 0, 0, 0, 1, 2, 2, 2, 2, 2, 0, 2, 2, 0, 2, 2, 2, 2, 2, 0, 0, 2, 2, 2, 0, 0, 2, 2, 2, 0, 2, 2, 0, 2, 0, 2, 0, 0, 2, 2, 0, 0, 2, 0, 0, 0, 0, 0, 0, 2, 0, 1, 2, 0, 2, 2, 2, 2, 2, 2, 0, 0, 1, 0, 0, 2, 2, 2, 0, 0, 0, 2, 2, 2, 2, 2, 0, 2, 2, 0, 0, 2, 2, 0, 0, 0, 0, 2, 2, 0, 0, 2, 2, 0, 0, 0, 2, 0, 2, 0, 0, 1, 2, 0, 2, 0, 0, 2, 2, 2, 2, 1, 2, 0, 0, 2, 2, 2, 0, 0, 2, 1, 0, 0, 0, 2, 2, 0, 2, 0, 0, 1, 2, 2, 2, 2, 2, 0, 2, 2, 0, 2, 0, 0, 1, 2, 0, 2, 2, 2, 0, 0]\n",
            "Labels in the tokenized validation dataset: [0, 2, 2, 2, 0, 0, 2, 2, 2, 0, 0, 0, 2, 2, 2, 2, 0, 0, 0, 1, 0, 1, 0, 0, 0, 2, 2, 0, 0, 0, 0, 2, 2, 2]\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# train the model\n",
        "trainer = Trainer(\n",
        "    model=model,\n",
        "    args=training_args,\n",
        "    train_dataset=tokenized_train_dataset,\n",
        "    eval_dataset=tokenized_validation_dataset,\n",
        "    compute_metrics=compute_metrics,\n",
        ")"
      ],
      "metadata": {
        "id": "uX5Mu37h4GD4"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "trainer.train()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 1000
        },
        "id": "RI9V_ptI4J3Q",
        "outputId": "27942a1a-2cfb-4700-9d5b-052b835fc9bf"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<IPython.core.display.HTML object>"
            ],
            "text/html": [
              "\n",
              "    <div>\n",
              "      \n",
              "      <progress value='6400' max='6400' style='width:300px; height:20px; vertical-align: middle;'></progress>\n",
              "      [6400/6400 13:33, Epoch 25/25]\n",
              "    </div>\n",
              "    <table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              " <tr style=\"text-align: left;\">\n",
              "      <th>Epoch</th>\n",
              "      <th>Training Loss</th>\n",
              "      <th>Validation Loss</th>\n",
              "      <th>Accuracy</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <td>1</td>\n",
              "      <td>No log</td>\n",
              "      <td>0.964452</td>\n",
              "      <td>0.529412</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>2</td>\n",
              "      <td>1.192600</td>\n",
              "      <td>0.857226</td>\n",
              "      <td>0.558824</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>3</td>\n",
              "      <td>1.192600</td>\n",
              "      <td>0.836332</td>\n",
              "      <td>0.558824</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>4</td>\n",
              "      <td>0.708500</td>\n",
              "      <td>0.834161</td>\n",
              "      <td>0.588235</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>5</td>\n",
              "      <td>0.708500</td>\n",
              "      <td>0.816424</td>\n",
              "      <td>0.647059</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>6</td>\n",
              "      <td>0.691900</td>\n",
              "      <td>0.798127</td>\n",
              "      <td>0.705882</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>7</td>\n",
              "      <td>0.691900</td>\n",
              "      <td>0.773724</td>\n",
              "      <td>0.676471</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>8</td>\n",
              "      <td>0.659000</td>\n",
              "      <td>0.764156</td>\n",
              "      <td>0.676471</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>9</td>\n",
              "      <td>0.659000</td>\n",
              "      <td>0.742923</td>\n",
              "      <td>0.676471</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>10</td>\n",
              "      <td>0.604200</td>\n",
              "      <td>0.733032</td>\n",
              "      <td>0.735294</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>11</td>\n",
              "      <td>0.604200</td>\n",
              "      <td>0.719892</td>\n",
              "      <td>0.735294</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>12</td>\n",
              "      <td>0.578400</td>\n",
              "      <td>0.720792</td>\n",
              "      <td>0.735294</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>13</td>\n",
              "      <td>0.578400</td>\n",
              "      <td>0.711786</td>\n",
              "      <td>0.735294</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>14</td>\n",
              "      <td>0.558600</td>\n",
              "      <td>0.718671</td>\n",
              "      <td>0.735294</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>15</td>\n",
              "      <td>0.558600</td>\n",
              "      <td>0.725040</td>\n",
              "      <td>0.735294</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>16</td>\n",
              "      <td>0.529400</td>\n",
              "      <td>0.714456</td>\n",
              "      <td>0.735294</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>17</td>\n",
              "      <td>0.529400</td>\n",
              "      <td>0.705688</td>\n",
              "      <td>0.735294</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>18</td>\n",
              "      <td>0.521200</td>\n",
              "      <td>0.707281</td>\n",
              "      <td>0.735294</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>19</td>\n",
              "      <td>0.521200</td>\n",
              "      <td>0.701411</td>\n",
              "      <td>0.735294</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>20</td>\n",
              "      <td>0.521200</td>\n",
              "      <td>0.711333</td>\n",
              "      <td>0.735294</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>21</td>\n",
              "      <td>0.521200</td>\n",
              "      <td>0.705395</td>\n",
              "      <td>0.735294</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>22</td>\n",
              "      <td>0.509100</td>\n",
              "      <td>0.709404</td>\n",
              "      <td>0.735294</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>23</td>\n",
              "      <td>0.509100</td>\n",
              "      <td>0.710098</td>\n",
              "      <td>0.735294</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>24</td>\n",
              "      <td>0.505900</td>\n",
              "      <td>0.711533</td>\n",
              "      <td>0.735294</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>25</td>\n",
              "      <td>0.505900</td>\n",
              "      <td>0.712224</td>\n",
              "      <td>0.735294</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table><p>"
            ]
          },
          "metadata": {}
        },
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "/usr/local/lib/python3.10/dist-packages/huggingface_hub/file_download.py:1132: FutureWarning: `resume_download` is deprecated and will be removed in version 1.0.0. Downloads always resume when possible. If you want to force a new download, use `force_download=True`.\n",
            "  warnings.warn(\n",
            "/usr/local/lib/python3.10/dist-packages/huggingface_hub/file_download.py:1132: FutureWarning: `resume_download` is deprecated and will be removed in version 1.0.0. Downloads always resume when possible. If you want to force a new download, use `force_download=True`.\n",
            "  warnings.warn(\n",
            "/usr/local/lib/python3.10/dist-packages/huggingface_hub/file_download.py:1132: FutureWarning: `resume_download` is deprecated and will be removed in version 1.0.0. Downloads always resume when possible. If you want to force a new download, use `force_download=True`.\n",
            "  warnings.warn(\n",
            "/usr/local/lib/python3.10/dist-packages/huggingface_hub/file_download.py:1132: FutureWarning: `resume_download` is deprecated and will be removed in version 1.0.0. Downloads always resume when possible. If you want to force a new download, use `force_download=True`.\n",
            "  warnings.warn(\n",
            "/usr/local/lib/python3.10/dist-packages/huggingface_hub/file_download.py:1132: FutureWarning: `resume_download` is deprecated and will be removed in version 1.0.0. Downloads always resume when possible. If you want to force a new download, use `force_download=True`.\n",
            "  warnings.warn(\n",
            "/usr/local/lib/python3.10/dist-packages/huggingface_hub/file_download.py:1132: FutureWarning: `resume_download` is deprecated and will be removed in version 1.0.0. Downloads always resume when possible. If you want to force a new download, use `force_download=True`.\n",
            "  warnings.warn(\n",
            "/usr/local/lib/python3.10/dist-packages/huggingface_hub/file_download.py:1132: FutureWarning: `resume_download` is deprecated and will be removed in version 1.0.0. Downloads always resume when possible. If you want to force a new download, use `force_download=True`.\n",
            "  warnings.warn(\n",
            "/usr/local/lib/python3.10/dist-packages/huggingface_hub/file_download.py:1132: FutureWarning: `resume_download` is deprecated and will be removed in version 1.0.0. Downloads always resume when possible. If you want to force a new download, use `force_download=True`.\n",
            "  warnings.warn(\n",
            "/usr/local/lib/python3.10/dist-packages/huggingface_hub/file_download.py:1132: FutureWarning: `resume_download` is deprecated and will be removed in version 1.0.0. Downloads always resume when possible. If you want to force a new download, use `force_download=True`.\n",
            "  warnings.warn(\n",
            "/usr/local/lib/python3.10/dist-packages/huggingface_hub/file_download.py:1132: FutureWarning: `resume_download` is deprecated and will be removed in version 1.0.0. Downloads always resume when possible. If you want to force a new download, use `force_download=True`.\n",
            "  warnings.warn(\n",
            "/usr/local/lib/python3.10/dist-packages/huggingface_hub/file_download.py:1132: FutureWarning: `resume_download` is deprecated and will be removed in version 1.0.0. Downloads always resume when possible. If you want to force a new download, use `force_download=True`.\n",
            "  warnings.warn(\n",
            "/usr/local/lib/python3.10/dist-packages/huggingface_hub/file_download.py:1132: FutureWarning: `resume_download` is deprecated and will be removed in version 1.0.0. Downloads always resume when possible. If you want to force a new download, use `force_download=True`.\n",
            "  warnings.warn(\n"
          ]
        },
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "TrainOutput(global_step=6400, training_loss=0.6235571074485778, metrics={'train_runtime': 816.1054, 'train_samples_per_second': 62.676, 'train_steps_per_second': 7.842, 'total_flos': 3366101591424000.0, 'train_loss': 0.6235571074485778, 'epoch': 25.0})"
            ]
          },
          "metadata": {},
          "execution_count": 30
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# Save the trained model and tokenizer to the specified directory\n",
        "model.save_pretrained(\"/content/drive/MyDrive/anewstart/karlancer/sentiment/results1\")\n",
        "tokenizer.save_pretrained(\"/content/drive/MyDrive/anewstart/karlancer/sentiment/results1\")"
      ],
      "metadata": {
        "id": "Dd8gWk1v4KTZ",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "ecb7f94a-5d1a-40d6-9702-53644138822a"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "/usr/local/lib/python3.10/dist-packages/huggingface_hub/file_download.py:1132: FutureWarning: `resume_download` is deprecated and will be removed in version 1.0.0. Downloads always resume when possible. If you want to force a new download, use `force_download=True`.\n",
            "  warnings.warn(\n"
          ]
        },
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "('/content/drive/MyDrive/anewstart/karlancer/sentiment/results1/tokenizer_config.json',\n",
              " '/content/drive/MyDrive/anewstart/karlancer/sentiment/results1/special_tokens_map.json',\n",
              " '/content/drive/MyDrive/anewstart/karlancer/sentiment/results1/vocab.txt',\n",
              " '/content/drive/MyDrive/anewstart/karlancer/sentiment/results1/added_tokens.json',\n",
              " '/content/drive/MyDrive/anewstart/karlancer/sentiment/results1/tokenizer.json')"
            ]
          },
          "metadata": {},
          "execution_count": 31
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "# inference"
      ],
      "metadata": {
        "id": "2EkC_oeov9ba"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "import torch\n",
        "from peft import PeftModel\n",
        "from transformers import  AutoModelForCausalLM, AutoTokenizer, AutoModelForSeq2SeqLM\n",
        "from transformers import BertForSequenceClassification\n"
      ],
      "metadata": {
        "id": "rOMSWtARwl5A"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "model_name_or_path = \"nimaafshar/parsbert-fa-sentiment-twitter\""
      ],
      "metadata": {
        "id": "ppLYVVR9QVY6"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "# Load the entire model on the GPU 0\n",
        "device_map = {\"\": 0}\n",
        "# fine-tuned model path\n",
        "path = \"/content/drive/MyDrive/anewstart/karlancer/sentiment/results1\"\n",
        "\n",
        "# base_model = AutoModelForSeq2SeqLM.from_pretrained(model_name_or_path)\n",
        "base_model = BertForSequenceClassification.from_pretrained(model_name_or_path, from_tf=True)\n",
        "# because I am using lora I need to use PeftModel to have my base model and fine-tuned model\n",
        "model = PeftModel.from_pretrained(base_model, path)\n",
        "\n",
        "tokenizer = AutoTokenizer.from_pretrained(model_name_or_path, trust_remote_code=True)"
      ],
      "metadata": {
        "id": "ddTX5jDHvI7X",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "43fe9a59-7b38-4f81-a9c7-55ecf4059c37"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "All TF 2.0 model weights were used when initializing BertForSequenceClassification.\n",
            "\n",
            "All the weights of BertForSequenceClassification were initialized from the TF 2.0 model.\n",
            "If your task is similar to the task the model of the checkpoint was trained on, you can already use BertForSequenceClassification for predictions without further training.\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "model"
      ],
      "metadata": {
        "collapsed": true,
        "id": "fepa3OCKxbiC"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "tokenizer"
      ],
      "metadata": {
        "id": "hsYPq_0N2UTY"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "# Example inputs\n",
        "inputs = tokenizer(\"سلام\", return_tensors='pt')\n",
        "\n",
        "# Print inputs to verify their structure\n",
        "print(inputs)\n",
        "\n",
        "# Forward pass through the model\n",
        "outputs = model(**inputs)\n",
        "\n",
        "# Example of accessing logits or any other output\n",
        "print(outputs)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "2zF3goe3SSPW",
        "outputId": "2f5dc6f9-5663-41ef-9579-097ec60a82a1"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "{'input_ids': tensor([[   2, 4285,    4]]), 'token_type_ids': tensor([[0, 0, 0]]), 'attention_mask': tensor([[1, 1, 1]])}\n",
            "SequenceClassifierOutput(loss=None, logits=tensor([[ 0.7383, -1.1728,  0.1496]], grad_fn=<AddmmBackward0>), hidden_states=None, attentions=None)\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "print(outputs.logits)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "0iQmv8CdTPLC",
        "outputId": "361d1f0c-a94f-4dfd-b03c-869c7107b13a"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "tensor([[ 0.7383, -1.1728,  0.1496]], grad_fn=<AddmmBackward0>)\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "classes = [\"negative\", \"neutral\", \"positive\"]\n",
        "sequences = [\n",
        "    \".غذا خیلی افتضاح بود متاسفم برای مدیریت رستورن خیلی بد بود.\",\n",
        "    \"خیلی خوشمزده و عالی بود عالی\",\n",
        "    \"می‌تونم اسمتونو بپرسم؟\",\n",
        "    \"بی شخصیت\"\n",
        "]\n",
        "\n",
        "# Iterate through sequences and make predictions\n",
        "for sequence in sequences:\n",
        "    # Tokenize input\n",
        "    inputs = tokenizer(sequence, return_tensors=\"pt\", max_length=128, padding=\"max_length\", truncation=True)\n",
        "\n",
        "    # Get logits from the model\n",
        "    with torch.no_grad():\n",
        "        outputs = model(**inputs)\n",
        "        logits = outputs.logits\n",
        "\n",
        "    # Apply softmax to get probabilities\n",
        "    probabilities = torch.nn.functional.softmax(logits, dim=1)\n",
        "\n",
        "    # Get predicted class\n",
        "    predicted_class_idx = torch.argmax(probabilities, dim=1).item()\n",
        "    predicted_class = classes[predicted_class_idx]\n",
        "\n",
        "    # Convert probabilities to NumPy array for printing\n",
        "    probabilities = probabilities.numpy()[0]\n",
        "\n",
        "    print(f\"Sequence: {sequence}\")\n",
        "    print(f\"Predicted class: {predicted_class}\")\n",
        "    print(f\"Probabilities: {probabilities}\")\n",
        "    print()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "jrXk1G6WT1cH",
        "outputId": "3d616d8f-7a88-4d2b-e691-1f1bfb1a5e79"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Sequence: .غذا خیلی افتضاح بود متاسفم برای مدیریت رستورن خیلی بد بود.\n",
            "Predicted class: negative\n",
            "Probabilities: [0.697853   0.16658133 0.13556565]\n",
            "\n",
            "Sequence: خیلی خوشمزده و عالی بود عالی\n",
            "Predicted class: positive\n",
            "Probabilities: [0.00593248 0.01492997 0.97913754]\n",
            "\n",
            "Sequence: می‌تونم اسمتونو بپرسم؟\n",
            "Predicted class: negative\n",
            "Probabilities: [0.80706173 0.05285115 0.14008713]\n",
            "\n",
            "Sequence: بی شخصیت\n",
            "Predicted class: negative\n",
            "Probabilities: [0.8420616  0.02046818 0.13747028]\n",
            "\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "classes = [\"negative\", \"neutral\", \"positive\"]\n",
        "sequences = [\n",
        "    \"یک فیلم با موضوع جدید و عجیب و شاید برای خیلیها غیرقابل باور ! دیدنش برام خیلی جذاب بود البته برای خیلیها هم نبود، اینکه در آخر فیلم از خودت میپرسی خب که چی؟! یکم حس سردرگمی بهت میداد و نمیزاشت همزادپنداری کنی با شخصیت اول فیلم! ولی در کل پیشنهاد\",\n",
        "    \"من خیلی دنبال این طمع گشتم تا تو دیجی پیداش کردم . من از طعمش خوشم اومد البته ممکنه کسی هم خوشش نیاد\",\n",
        "    \"دوستان حتما دقت کنید درقسمت فیله تکه های سبز رنگ داشت که نشان دهنده انتیبیوتیک هایی هست که به مرغ ها میدن و اون تکه سمیه. بیشترشم استخونه من راضی نبودم و دیگه ازین مارو خرید نمیکنم\",\n",
        "\n",
        "]\n",
        "\n",
        "# Iterate through sequences and make predictions\n",
        "for sequence in sequences:\n",
        "    # Tokenize input\n",
        "    inputs = tokenizer(sequence, return_tensors=\"pt\", max_length=128, padding=\"max_length\", truncation=True)\n",
        "\n",
        "    # Get logits from the model\n",
        "    with torch.no_grad():\n",
        "        outputs = model(**inputs)\n",
        "        logits = outputs.logits\n",
        "\n",
        "    # Apply softmax to get probabilities\n",
        "    probabilities = torch.nn.functional.softmax(logits, dim=1)\n",
        "\n",
        "    # Get predicted class\n",
        "    predicted_class_idx = torch.argmax(probabilities, dim=1).item()\n",
        "    predicted_class = classes[predicted_class_idx]\n",
        "\n",
        "    # Convert probabilities to NumPy array for printing\n",
        "    probabilities = probabilities.numpy()[0]\n",
        "\n",
        "    print(f\"Sequence: {sequence}\")\n",
        "    print(f\"Predicted class: {predicted_class}\")\n",
        "    print(f\"Probabilities: {probabilities}\")\n",
        "    print()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "K4TB8EUvUMRa",
        "outputId": "b1b2384d-a47f-4f92-c028-326f8c6bad17"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Sequence: یک فیلم با موضوع جدید و عجیب و شاید برای خیلیها غیرقابل باور ! دیدنش برام خیلی جذاب بود البته برای خیلیها هم نبود، اینکه در آخر فیلم از خودت میپرسی خب که چی؟! یکم حس سردرگمی بهت میداد و نمیزاشت همزادپنداری کنی با شخصیت اول فیلم! ولی در کل پیشنهاد\n",
            "Predicted class: negative\n",
            "Probabilities: [0.6923772  0.01759712 0.29002565]\n",
            "\n",
            "Sequence: من خیلی دنبال این طمع گشتم تا تو دیجی پیداش کردم . من از طعمش خوشم اومد البته ممکنه کسی هم خوشش نیاد\n",
            "Predicted class: negative\n",
            "Probabilities: [0.5211526  0.02084494 0.45800242]\n",
            "\n",
            "Sequence: دوستان حتما دقت کنید درقسمت فیله تکه های سبز رنگ داشت که نشان دهنده انتیبیوتیک هایی هست که به مرغ ها میدن و اون تکه سمیه. بیشترشم استخونه من راضی نبودم و دیگه ازین مارو خرید نمیکنم\n",
            "Predicted class: negative\n",
            "Probabilities: [0.92443126 0.02211499 0.05345375]\n",
            "\n"
          ]
        }
      ]
    }
  ]
}