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  },
  "cells": [
    {
      "cell_type": "code",
      "execution_count": 1,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "hxpmMU-UZuAA",
        "outputId": "ba9d830f-26d6-436f-fc49-4a49efedcf04"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "\u001b[?25l     \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m0.0/507.1 kB\u001b[0m \u001b[31m?\u001b[0m eta \u001b[36m-:--:--\u001b[0m\r\u001b[2K     \u001b[91m━━━━━━━━━━━━━\u001b[0m\u001b[91m╸\u001b[0m\u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m174.1/507.1 kB\u001b[0m \u001b[31m5.2 MB/s\u001b[0m eta \u001b[36m0:00:01\u001b[0m\r\u001b[2K     \u001b[91m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[91m╸\u001b[0m \u001b[32m501.8/507.1 kB\u001b[0m \u001b[31m8.3 MB/s\u001b[0m eta \u001b[36m0:00:01\u001b[0m\r\u001b[2K     \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m507.1/507.1 kB\u001b[0m \u001b[31m6.6 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[31m9.0 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n",
            "\u001b[2K     \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m270.9/270.9 kB\u001b[0m \u001b[31m10.1 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n",
            "\u001b[2K     \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m115.3/115.3 kB\u001b[0m \u001b[31m9.4 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[31m12.3 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[31m3.3 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n",
            "\u001b[?25h"
          ]
        }
      ],
      "source": [
        "!pip install --quiet transformers datasets evaluate accelerate"
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "!gdown 1oUKScMHKFS0UfgVVbb1sPpcF_B4BNco4 -O dataset.zip\n",
        "!unzip -o dataset.zip -d dataset\n",
        "!rm dataset.zip"
      ],
      "metadata": {
        "id": "BnXcsgssaGaf",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "bc63bf1a-c086-4899-9f6c-b9fcc7014968"
      },
      "execution_count": 2,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Downloading...\n",
            "From: https://drive.google.com/uc?id=1oUKScMHKFS0UfgVVbb1sPpcF_B4BNco4\n",
            "To: /content/dataset.zip\n",
            "\r  0% 0.00/503k [00:00<?, ?B/s]\r100% 503k/503k [00:00<00:00, 5.26MB/s]\n",
            "Archive:  dataset.zip\n",
            "  inflating: dataset/test.csv        \n",
            "  inflating: dataset/train.csv       \n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "from transformers import AutoTokenizer, DataCollatorWithPadding, AutoModelForSequenceClassification, TrainingArguments, Trainer, pipeline, AutoConfig\n",
        "import evaluate\n",
        "from datasets import load_dataset\n",
        "import pandas as pd\n",
        "import numpy as np\n",
        "import matplotlib.pyplot as plt\n",
        "from sklearn.metrics import precision_score, recall_score, f1_score, confusion_matrix, classification_report\n",
        "import seaborn as sns"
      ],
      "metadata": {
        "id": "RJzTvK7ScZkT"
      },
      "execution_count": 3,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "MODEL_NAME=\"HooshvareLab/bert-fa-base-uncased\"\n",
        "MODEL_NAME=\"FacebookAI/xlm-roberta-large\"\n",
        "MODEL_NAME=\"pedramyazdipoor/persian_xlm_roberta_large\"\n",
        "MODEL_NAME=\"wietsedv/xlm-roberta-base-ft-udpos28-fa\"\n",
        "MODEL_NAME=\"HooshvareLab/albert-fa-zwnj-base-v2\"\n",
        "# MODEL_NAME=\"HooshvareLab/roberta-fa-zwnj-base\"\n",
        "MODEL_NAME=\"m3hrdadfi/albert-fa-base-v2-clf-persiannews\"\n",
        "MODEL_NAME=\"wietsedv/xlm-roberta-base-ft-udpos28-fa\"\n",
        "MODEL_NAME=\"FacebookAI/xlm-roberta-base\"\n",
        "MODEL_NAME=\"pedramyazdipoor/persian_xlm_roberta_large\""
      ],
      "metadata": {
        "id": "7HdyAr5HuVMK"
      },
      "execution_count": 4,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "tokenizer = AutoTokenizer.from_pretrained(MODEL_NAME)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 325,
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        "id": "s7Y1YTcUdEwO",
        "outputId": "4aa8fffe-2eee-4bcb-e73f-ce602ee41901"
      },
      "execution_count": 5,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "/usr/local/lib/python3.10/dist-packages/huggingface_hub/utils/_token.py:88: 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": [
              "tokenizer_config.json:   0%|          | 0.00/621 [00:00<?, ?B/s]"
            ],
            "application/vnd.jupyter.widget-view+json": {
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          },
          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "sentencepiece.bpe.model:   0%|          | 0.00/5.07M [00:00<?, ?B/s]"
            ],
            "application/vnd.jupyter.widget-view+json": {
              "version_major": 2,
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          },
          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "tokenizer.json:   0%|          | 0.00/17.1M [00:00<?, ?B/s]"
            ],
            "application/vnd.jupyter.widget-view+json": {
              "version_major": 2,
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          },
          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "special_tokens_map.json:   0%|          | 0.00/167 [00:00<?, ?B/s]"
            ],
            "application/vnd.jupyter.widget-view+json": {
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      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# def preprocess_function(dataframe):\n",
        "#     return tokenizer(dataframe[\"text\"].tolist(), padding=True, truncation=True, return_tensors=\"pt\")\n",
        "def preprocess_function(data):\n",
        "    for i in range(len(data[\"label\"])):\n",
        "      data[\"label\"][i]=label2id[data[\"label\"][i]]\n",
        "    # return tokenizer(data[\"text\"],add_special_tokens = True,\n",
        "    #                  return_token_type_ids = True,\n",
        "    #                  return_tensors = 'pt',\n",
        "    #                  padding = True,\n",
        "    #                  return_offsets_mapping = True,\n",
        "    #                  truncation = 'only_first',\n",
        "    #                  max_length = 32)\n",
        "    return tokenizer(data[\"text\"], truncation=True,padding=True,max_length=50, add_special_tokens = True)"
      ],
      "metadata": {
        "id": "fiaVrgnEdM34"
      },
      "execution_count": 6,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "TRAIN_FILE=\"dataset/train.csv\"\n",
        "TEST_FILE=\"dataset/test.csv\""
      ],
      "metadata": {
        "id": "NhGLpwbdoTWK"
      },
      "execution_count": 7,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "train_df= pd.read_csv(TRAIN_FILE)\n",
        "test_df= pd.read_csv(TEST_FILE)"
      ],
      "metadata": {
        "id": "agljSqNGiifs"
      },
      "execution_count": 8,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "dataset = load_dataset('csv', data_files={'train': TRAIN_FILE,'test': TEST_FILE})"
      ],
      "metadata": {
        "id": "fXvK7jK5oGHK",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 81,
          "referenced_widgets": [
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        "outputId": "e23e02f7-d680-4e57-9e0d-b3480fa41b31"
      },
      "execution_count": 9,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "Generating train split: 0 examples [00:00, ? examples/s]"
            ],
            "application/vnd.jupyter.widget-view+json": {
              "version_major": 2,
              "version_minor": 0,
              "model_id": "91e68eb5d7ee4efcb3439de5c4e3a5ac"
            }
          },
          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "Generating test split: 0 examples [00:00, ? examples/s]"
            ],
            "application/vnd.jupyter.widget-view+json": {
              "version_major": 2,
              "version_minor": 0,
              "model_id": "6089c2068fc847a1a5bb387c86030106"
            }
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "id2label = {}\n",
        "label2id = {}\n",
        "#TODO: use dataset[\"train\"][\"label\"].unique()\n",
        "emotions=train_df[\"label\"].unique()\n",
        "index=0\n",
        "for emotion in emotions:\n",
        "  id2label[index]=emotion\n",
        "  label2id[emotion]=index\n",
        "  index+=1\n",
        "num_classes=len(emotions)"
      ],
      "metadata": {
        "id": "OGmvyyafeJqD"
      },
      "execution_count": 10,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "tokenized_dataset = dataset.map(preprocess_function, batched=True)"
      ],
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            "64f7303077894f62ad297440f8fe8c27"
          ]
        },
        "outputId": "3bb1cdaf-8a1b-46c7-befb-7e78a51694e1"
      },
      "execution_count": 11,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "Map:   0%|          | 0/6125 [00:00<?, ? examples/s]"
            ],
            "application/vnd.jupyter.widget-view+json": {
              "version_major": 2,
              "version_minor": 0,
              "model_id": "cf585b1780094a8a9e608f0422a6abdc"
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          },
          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "Map:   0%|          | 0/1151 [00:00<?, ? examples/s]"
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            "application/vnd.jupyter.widget-view+json": {
              "version_major": 2,
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              "model_id": "4233988c34bc479b87a6e9c59a42dbf1"
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          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "tokenized_dataset[\"train\"][2]"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "mJEVeX-CsKuj",
        "outputId": "3826f09f-8951-4619-950b-d2c406a6bfc7"
      },
      "execution_count": 12,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "{'text': '\" کیفیتش خیلی خوبه با شک خریدم ولی واقعا راضیم بعد از حدود 2 ماه استفاده « متاسفانه باخبر شدیم » که فردی در ایرانشهر به حداقل 41 دختر تجاوز کرده . امیدواریم با همکاری نمایندگان محترم مجلس و دستگاه قضا ، دیگه به این راحتی باخبر نشیم .',\n",
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            ]
          },
          "metadata": {},
          "execution_count": 12
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "data_collator = DataCollatorWithPadding(tokenizer=tokenizer)"
      ],
      "metadata": {
        "id": "WgBxUHNkeBWg"
      },
      "execution_count": 13,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "config = AutoConfig.from_pretrained(\"HooshvareLab/albert-fa-zwnj-base-v2\")\n",
        "config.update({\n",
        "    \"num_labels\":num_classes,\n",
        "    \"id2label\":id2label,\n",
        "    \"label2id\":label2id\n",
        "})"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 49,
          "referenced_widgets": [
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          ]
        },
        "id": "cETafsH_plPO",
        "outputId": "bea7a135-33ae-42c7-c6ff-98839b4c5cea"
      },
      "execution_count": 14,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "config.json:   0%|          | 0.00/760 [00:00<?, ?B/s]"
            ],
            "application/vnd.jupyter.widget-view+json": {
              "version_major": 2,
              "version_minor": 0,
              "model_id": "75c3d479702d4625b1d854f1da1a0f2a"
            }
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# model = AutoModelForSequenceClassification.from_pretrained(\n",
        "#     MODEL_NAME, config=config\n",
        "# )"
      ],
      "metadata": {
        "id": "mWyyxcDkvrKU"
      },
      "execution_count": 15,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "model = AutoModelForSequenceClassification.from_pretrained(\n",
        "    MODEL_NAME, num_labels=num_classes, id2label=id2label, label2id=label2id\n",
        ")"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 164,
          "referenced_widgets": [
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            "fa0f6526150044ad8d94d4751b90a5c0"
          ]
        },
        "id": "Tb4vzdxUe8vk",
        "outputId": "dc52abfd-8ed0-4ea8-feec-c6405f831d9f"
      },
      "execution_count": 16,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "config.json:   0%|          | 0.00/821 [00:00<?, ?B/s]"
            ],
            "application/vnd.jupyter.widget-view+json": {
              "version_major": 2,
              "version_minor": 0,
              "model_id": "89e4793b0a3941cf86f807e0c4bb4531"
            }
          },
          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "pytorch_model.bin:   0%|          | 0.00/2.24G [00:00<?, ?B/s]"
            ],
            "application/vnd.jupyter.widget-view+json": {
              "version_major": 2,
              "version_minor": 0,
              "model_id": "39c7d73d76be4d15b7741fe9dbea2b9d"
            }
          },
          "metadata": {}
        },
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "Some weights of XLMRobertaForSequenceClassification were not initialized from the model checkpoint at pedramyazdipoor/persian_xlm_roberta_large and are newly initialized: ['classifier.dense.bias', 'classifier.out_proj.weight', 'classifier.out_proj.bias', 'classifier.dense.weight']\n",
            "You should probably TRAIN this model on a down-stream task to be able to use it for predictions and inference.\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "accuracy = evaluate.load(\"accuracy\")\n",
        "def compute_metrics(eval_pred):\n",
        "    predictions, labels = eval_pred\n",
        "    predictions = np.argmax(predictions, axis=1)\n",
        "    return accuracy.compute(predictions=predictions, references=labels)"
      ],
      "metadata": {
        "id": "Xz2HN_Kqg7UK",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 77,
          "referenced_widgets": [
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            "a980aca6072e493da0d2493195a3af8d"
          ]
        },
        "outputId": "8a5ef28a-7891-4238-b2ac-d08bb0578b2c"
      },
      "execution_count": 17,
      "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": "b558bcb7de9f4ef187dfd1ef1cd166da"
            }
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# training_args = TrainingArguments(\n",
        "#     output_dir=\"my_awesome_model\",\n",
        "#     learning_rate=3e-5,\n",
        "#     # max_seq_len = 256,\n",
        "#     gradient_accumulation_steps = 8,\n",
        "#     per_device_train_batch_size=4,\n",
        "#     per_device_eval_batch_size=4,\n",
        "#     num_train_epochs=2,\n",
        "#     weight_decay=0.01,\n",
        "#     evaluation_strategy=\"epoch\",\n",
        "#     save_strategy=\"epoch\",\n",
        "#     load_best_model_at_end=True,\n",
        "#     push_to_hub=False,\n",
        "# )\n",
        "training_args = TrainingArguments(\n",
        "    output_dir=\"my_awesome_model\",\n",
        "    learning_rate=6e-6,\n",
        "    per_device_train_batch_size=64,\n",
        "    per_device_eval_batch_size=16,\n",
        "    num_train_epochs=15,\n",
        "    weight_decay=0.01,\n",
        "    evaluation_strategy=\"epoch\",\n",
        "    save_strategy=\"epoch\",\n",
        "    load_best_model_at_end=True,\n",
        "    push_to_hub=False,\n",
        ")"
      ],
      "metadata": {
        "id": "RpGciXzjg8tA"
      },
      "execution_count": 18,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "trainer = Trainer(\n",
        "    model=model,\n",
        "    args=training_args,\n",
        "    train_dataset=tokenized_dataset[\"train\"],\n",
        "    eval_dataset=tokenized_dataset[\"test\"],\n",
        "    tokenizer=tokenizer,\n",
        "    data_collator=data_collator,\n",
        "    compute_metrics=compute_metrics,\n",
        ")"
      ],
      "metadata": {
        "id": "xL9hNiVrgJhg"
      },
      "execution_count": 19,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "trainer.train()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 893
        },
        "id": "4Jut8nzQgsMc",
        "outputId": "c96a9326-db59-4531-882e-6b82895eb659"
      },
      "execution_count": 20,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "You're using a XLMRobertaTokenizerFast tokenizer. Please note that with a fast tokenizer, using the `__call__` method is faster than using a method to encode the text followed by a call to the `pad` method to get a padded encoding.\n"
          ]
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<IPython.core.display.HTML object>"
            ],
            "text/html": [
              "\n",
              "    <div>\n",
              "      \n",
              "      <progress value='769' max='1440' style='width:300px; height:20px; vertical-align: middle;'></progress>\n",
              "      [ 769/1440 36:20 < 31:47, 0.35 it/s, Epoch 8/15]\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>1.968679</td>\n",
              "      <td>0.172893</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>2</td>\n",
              "      <td>No log</td>\n",
              "      <td>1.666397</td>\n",
              "      <td>0.361425</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>3</td>\n",
              "      <td>No log</td>\n",
              "      <td>1.247379</td>\n",
              "      <td>0.589922</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>4</td>\n",
              "      <td>No log</td>\n",
              "      <td>1.134480</td>\n",
              "      <td>0.635969</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>5</td>\n",
              "      <td>No log</td>\n",
              "      <td>1.049189</td>\n",
              "      <td>0.650738</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>6</td>\n",
              "      <td>1.361600</td>\n",
              "      <td>0.992559</td>\n",
              "      <td>0.674196</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>7</td>\n",
              "      <td>1.361600</td>\n",
              "      <td>1.014308</td>\n",
              "      <td>0.667246</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>8</td>\n",
              "      <td>1.361600</td>\n",
              "      <td>0.998116</td>\n",
              "      <td>0.683753</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table><p>"
            ]
          },
          "metadata": {}
        },
        {
          "output_type": "error",
          "ename": "RuntimeError",
          "evalue": "[enforce fail at inline_container.cc:424] . unexpected pos 4013857728 vs 4013857616",
          "traceback": [
            "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
            "\u001b[0;31mRuntimeError\u001b[0m                              Traceback (most recent call last)",
            "\u001b[0;32m/usr/local/lib/python3.10/dist-packages/torch/serialization.py\u001b[0m in \u001b[0;36msave\u001b[0;34m(obj, f, pickle_module, pickle_protocol, _use_new_zipfile_serialization, _disable_byteorder_record)\u001b[0m\n\u001b[1;32m    618\u001b[0m         \u001b[0;32mwith\u001b[0m \u001b[0m_open_zipfile_writer\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mf\u001b[0m\u001b[0;34m)\u001b[0m \u001b[0;32mas\u001b[0m \u001b[0mopened_zipfile\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 619\u001b[0;31m             \u001b[0m_save\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mobj\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mopened_zipfile\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mpickle_module\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mpickle_protocol\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0m_disable_byteorder_record\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    620\u001b[0m             \u001b[0;32mreturn\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
            "\u001b[0;32m/usr/local/lib/python3.10/dist-packages/torch/serialization.py\u001b[0m in \u001b[0;36m_save\u001b[0;34m(obj, zip_file, pickle_module, pickle_protocol, _disable_byteorder_record)\u001b[0m\n\u001b[1;32m    852\u001b[0m         \u001b[0mnum_bytes\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mstorage\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mnbytes\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 853\u001b[0;31m         \u001b[0mzip_file\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mwrite_record\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mname\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mstorage\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mdata_ptr\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mnum_bytes\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    854\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n",
            "\u001b[0;31mRuntimeError\u001b[0m: [enforce fail at inline_container.cc:588] . PytorchStreamWriter failed writing file data/422: file write failed",
            "\nDuring handling of the above exception, another exception occurred:\n",
            "\u001b[0;31mRuntimeError\u001b[0m                              Traceback (most recent call last)",
            "\u001b[0;32m<ipython-input-20-3435b262f1ae>\u001b[0m in \u001b[0;36m<cell line: 1>\u001b[0;34m()\u001b[0m\n\u001b[0;32m----> 1\u001b[0;31m \u001b[0mtrainer\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mtrain\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m",
            "\u001b[0;32m/usr/local/lib/python3.10/dist-packages/transformers/trainer.py\u001b[0m in \u001b[0;36mtrain\u001b[0;34m(self, resume_from_checkpoint, trial, ignore_keys_for_eval, **kwargs)\u001b[0m\n\u001b[1;32m   1553\u001b[0m                 \u001b[0mhf_hub_utils\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0menable_progress_bars\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   1554\u001b[0m         \u001b[0;32melse\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m-> 1555\u001b[0;31m             return inner_training_loop(\n\u001b[0m\u001b[1;32m   1556\u001b[0m                 \u001b[0margs\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0margs\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   1557\u001b[0m                 \u001b[0mresume_from_checkpoint\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mresume_from_checkpoint\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
            "\u001b[0;32m/usr/local/lib/python3.10/dist-packages/transformers/trainer.py\u001b[0m in \u001b[0;36m_inner_training_loop\u001b[0;34m(self, batch_size, args, resume_from_checkpoint, trial, ignore_keys_for_eval)\u001b[0m\n\u001b[1;32m   1935\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   1936\u001b[0m             \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mcontrol\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mcallback_handler\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mon_epoch_end\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0margs\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mstate\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mcontrol\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m-> 1937\u001b[0;31m             \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_maybe_log_save_evaluate\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mtr_loss\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mmodel\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mtrial\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mepoch\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mignore_keys_for_eval\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m   1938\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   1939\u001b[0m             \u001b[0;32mif\u001b[0m \u001b[0mDebugOption\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mTPU_METRICS_DEBUG\u001b[0m \u001b[0;32min\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0margs\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mdebug\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
            "\u001b[0;32m/usr/local/lib/python3.10/dist-packages/transformers/trainer.py\u001b[0m in \u001b[0;36m_maybe_log_save_evaluate\u001b[0;34m(self, tr_loss, model, trial, epoch, ignore_keys_for_eval)\u001b[0m\n\u001b[1;32m   2280\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   2281\u001b[0m         \u001b[0;32mif\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mcontrol\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mshould_save\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m-> 2282\u001b[0;31m             \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_save_checkpoint\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mmodel\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mtrial\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mmetrics\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mmetrics\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m   2283\u001b[0m             \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mcontrol\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mcallback_handler\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mon_save\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0margs\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mstate\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mcontrol\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   2284\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n",
            "\u001b[0;32m/usr/local/lib/python3.10/dist-packages/transformers/trainer.py\u001b[0m in \u001b[0;36m_save_checkpoint\u001b[0;34m(self, model, trial, metrics)\u001b[0m\n\u001b[1;32m   2385\u001b[0m         \u001b[0;32melif\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0margs\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mshould_save\u001b[0m \u001b[0;32mand\u001b[0m \u001b[0;32mnot\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mis_deepspeed_enabled\u001b[0m \u001b[0;32mand\u001b[0m \u001b[0;32mnot\u001b[0m \u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mfsdp\u001b[0m \u001b[0;32mor\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mis_fsdp_enabled\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   2386\u001b[0m             \u001b[0;31m# deepspeed.save_checkpoint above saves model/optim/sched\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m-> 2387\u001b[0;31m             \u001b[0mtorch\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0msave\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0moptimizer\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mstate_dict\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mos\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mpath\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mjoin\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0moutput_dir\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mOPTIMIZER_NAME\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m   2388\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   2389\u001b[0m         \u001b[0;31m# Save SCHEDULER & SCALER\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
            "\u001b[0;32m/usr/local/lib/python3.10/dist-packages/torch/serialization.py\u001b[0m in \u001b[0;36msave\u001b[0;34m(obj, f, pickle_module, pickle_protocol, _use_new_zipfile_serialization, _disable_byteorder_record)\u001b[0m\n\u001b[1;32m    616\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    617\u001b[0m     \u001b[0;32mif\u001b[0m \u001b[0m_use_new_zipfile_serialization\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 618\u001b[0;31m         \u001b[0;32mwith\u001b[0m \u001b[0m_open_zipfile_writer\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mf\u001b[0m\u001b[0;34m)\u001b[0m \u001b[0;32mas\u001b[0m \u001b[0mopened_zipfile\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    619\u001b[0m             \u001b[0m_save\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mobj\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mopened_zipfile\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mpickle_module\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mpickle_protocol\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0m_disable_byteorder_record\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    620\u001b[0m             \u001b[0;32mreturn\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
            "\u001b[0;32m/usr/local/lib/python3.10/dist-packages/torch/serialization.py\u001b[0m in \u001b[0;36m__exit__\u001b[0;34m(self, *args)\u001b[0m\n\u001b[1;32m    464\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    465\u001b[0m     \u001b[0;32mdef\u001b[0m \u001b[0m__exit__\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m*\u001b[0m\u001b[0margs\u001b[0m\u001b[0;34m)\u001b[0m \u001b[0;34m->\u001b[0m \u001b[0;32mNone\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 466\u001b[0;31m         \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mfile_like\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mwrite_end_of_file\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    467\u001b[0m         \u001b[0;32mif\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mfile_stream\u001b[0m \u001b[0;32mis\u001b[0m \u001b[0;32mnot\u001b[0m \u001b[0;32mNone\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    468\u001b[0m             \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mfile_stream\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mclose\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
            "\u001b[0;31mRuntimeError\u001b[0m: [enforce fail at inline_container.cc:424] . unexpected pos 4013857728 vs 4013857616"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "trainer.save_model(\"save_best_model\")"
      ],
      "metadata": {
        "id": "oQsS5HVe2-G2"
      },
      "execution_count": 22,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "classifier = pipeline(\n",
        "    \"text-classification\",\n",
        "    model=model,\n",
        "    tokenizer=tokenizer,\n",
        "    return_all_scores=True,\n",
        "    device=0\n",
        ")\n",
        "def predict(text):\n",
        "  return classifier(text)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "V5a8-Kyqjwbb",
        "outputId": "d4c9cf16-ba0f-4248-d097-d52c4d5bd7bd"
      },
      "execution_count": 44,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "/usr/local/lib/python3.10/dist-packages/transformers/pipelines/text_classification.py:105: UserWarning: `return_all_scores` is now deprecated,  if want a similar functionality use `top_k=None` instead of `return_all_scores=True` or `top_k=1` instead of `return_all_scores=False`.\n",
            "  warnings.warn(\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "text=\"امروز چه روز غم انگیزیه\""
      ],
      "metadata": {
        "id": "b1QIIkl-krIx"
      },
      "execution_count": 24,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "tokenizer(text)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "l72Z6ZGVBPoM",
        "outputId": "3fe0c3f2-f7cf-4243-d00a-2ceffd9d44c8"
      },
      "execution_count": 25,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "{'input_ids': [0, 15199, 6310, 3351, 59396, 70000, 11558, 2], 'attention_mask': [1, 1, 1, 1, 1, 1, 1, 1]}"
            ]
          },
          "metadata": {},
          "execution_count": 25
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "predict(text)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "C1DYrMSCkxtY",
        "outputId": "72e47155-df3a-4f80-bfff-fb7c2b3f8fe4"
      },
      "execution_count": 26,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "[[{'label': 'SAD', 'score': 0.8976266980171204},\n",
              "  {'label': 'HATE', 'score': 0.04853537306189537},\n",
              "  {'label': 'OTHER', 'score': 0.009536751545965672},\n",
              "  {'label': 'FEAR', 'score': 0.010307797230780125},\n",
              "  {'label': 'ANGRY', 'score': 0.006410748697817326},\n",
              "  {'label': 'HAPPY', 'score': 0.018891779705882072},\n",
              "  {'label': 'SURPRISE', 'score': 0.008690740913152695}]]"
            ]
          },
          "metadata": {},
          "execution_count": 26
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "history=pd.DataFrame(trainer.state.log_history)"
      ],
      "metadata": {
        "id": "syrYWIIAmMPz"
      },
      "execution_count": 27,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "val_acc=history[[\"epoch\",\"eval_accuracy\"]].dropna()\n",
        "val_loss=history[[\"epoch\",\"eval_loss\"]].dropna()\n",
        "train_loss=history[[\"epoch\",\"loss\"]].dropna()\n",
        "learning_rates=history[[\"epoch\",\"learning_rate\"]].dropna()"
      ],
      "metadata": {
        "id": "ztzzKteWnZhX"
      },
      "execution_count": 28,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "plt.plot(val_acc[\"epoch\"],val_acc[\"eval_accuracy\"], label=\"Accuracy\")\n",
        "plt.xlabel(\"Epoch\")\n",
        "plt.ylabel(\"Accuracy\")\n",
        "plt.title(\"Val Accuracy per Epoch\")\n",
        "plt.legend()\n",
        "plt.show()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 472
        },
        "id": "39cdQ38DokBJ",
        "outputId": "1aeaa231-c52f-4972-b19d-bb16b9323db1"
      },
      "execution_count": 29,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 640x480 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "plt.plot(val_loss[\"epoch\"],val_loss[\"eval_loss\"], label=\"Val Loss\")\n",
        "plt.plot(train_loss[\"epoch\"],train_loss[\"loss\"], label=\"Train Loss\")\n",
        "plt.xlabel(\"Epoch\")\n",
        "plt.ylabel(\"Loss\")\n",
        "plt.title(\"Loss per Epoch\")\n",
        "plt.legend()\n",
        "plt.show()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 472
        },
        "id": "SgrWvFByo1XD",
        "outputId": "728252fb-e351-4c2b-8de7-8c84ce5d902d"
      },
      "execution_count": 30,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 640x480 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "plt.plot(learning_rates[\"epoch\"],learning_rates[\"learning_rate\"], label=\"Learning rate\")\n",
        "plt.xlabel(\"epoch\")\n",
        "plt.ylabel(\"alpha\")\n",
        "plt.title(\"Learning rate per Epoch\")\n",
        "plt.legend()\n",
        "plt.show()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 472
        },
        "id": "Gqwa-WfGpHnd",
        "outputId": "6d82a4c6-9260-4586-95d5-4f9b26095809"
      },
      "execution_count": 71,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 640x480 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# from google.colab import drive\n",
        "# drive.mount('/content/drive')"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "qBn3P2qtB7Sf",
        "outputId": "6c9989b4-0e34-44a7-a154-7a4fef7e5a25"
      },
      "execution_count": 32,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Mounted at /content/drive\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# !cp -r save_best_model/ drive/MyDrive/DL_Project"
      ],
      "metadata": {
        "id": "iH8A2BWJB78x"
      },
      "execution_count": 34,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "classifier_best_score = pipeline(\n",
        "    \"text-classification\",\n",
        "    model=model,\n",
        "    tokenizer=tokenizer,\n",
        "    return_all_scores=False,\n",
        "    device=0\n",
        ")"
      ],
      "metadata": {
        "id": "zysnojvTINp5"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "predictions=classifier_best_score(list(test_df[\"text\"]))"
      ],
      "metadata": {
        "id": "jAKpx2cPGwYJ"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "Y_test=list(test_df['label'])\n",
        "Y_pred=[]\n",
        "for pred in predictions:\n",
        "  Y_pred.append(pred[\"label\"])"
      ],
      "metadata": {
        "id": "ilkJOmkuG9iD"
      },
      "execution_count": 61,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "def plot_confusion_matrix(y_test,y_scores, classNames):\n",
        "    # y_test=np.argmax(y_test, axis=1)\n",
        "    # y_scores=np.argmax(y_scores, axis=1)\n",
        "    classes = len(classNames)\n",
        "    cm = confusion_matrix(y_test, y_scores)\n",
        "    print(\"**** Confusion Matrix ****\")\n",
        "    print(cm)\n",
        "    print(\"**** Classification Report ****\")\n",
        "    print(classification_report(y_test, y_scores, target_names=classNames))\n",
        "    con = np.zeros((classes,classes))\n",
        "    for x in range(classes):\n",
        "        for y in range(classes):\n",
        "            con[x,y] = cm[x,y]/np.sum(cm[x,:])\n",
        "\n",
        "    plt.figure(figsize=(40,40))\n",
        "    sns.set(font_scale=3.0) # for label size\n",
        "    df = sns.heatmap(con, annot=True,fmt='.2', cmap='Blues',xticklabels= classNames , yticklabels= classNames)\n",
        "    df.figure.savefig(\"image2.png\")\n"
      ],
      "metadata": {
        "id": "KXkajqLnNZS8"
      },
      "execution_count": 74,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "plot_confusion_matrix(Y_test,Y_pred, emotions)\n"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 1000
        },
        "id": "5uJfXlOaQNw_",
        "outputId": "5430f3e9-b322-4e40-9fe3-9918adf28b58"
      },
      "execution_count": 75,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "**** Confusion Matrix ****\n",
            "[[ 97   5   2   2  18  24   6]\n",
            " [  2  45   1   0   0   8   1]\n",
            " [  4   4 207   2  25  29   4]\n",
            " [ 16   4   0  34   2   9   0]\n",
            " [ 14   1   5   3 138  30   2]\n",
            " [  3   3   8  11   9 228   0]\n",
            " [ 15   1   7   1  18  33  70]]\n",
            "**** Classification Report ****\n",
            "              precision    recall  f1-score   support\n",
            "\n",
            "         SAD       0.64      0.63      0.64       154\n",
            "        HATE       0.71      0.79      0.75        57\n",
            "       OTHER       0.90      0.75      0.82       275\n",
            "        FEAR       0.64      0.52      0.58        65\n",
            "       ANGRY       0.66      0.72      0.68       193\n",
            "       HAPPY       0.63      0.87      0.73       262\n",
            "    SURPRISE       0.84      0.48      0.61       145\n",
            "\n",
            "    accuracy                           0.71      1151\n",
            "   macro avg       0.72      0.68      0.69      1151\n",
            "weighted avg       0.73      0.71      0.71      1151\n",
            "\n"
          ]
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 4000x4000 with 2 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "average=\"weighted\"\n",
        "precision = precision_score(Y_test, Y_pred,average=average)\n",
        "recall = recall_score(Y_test, Y_pred,average=average)\n",
        "f1 = f1_score(Y_test, Y_pred,average=average)\n",
        "\n",
        "print(\"Precision: \", precision)\n",
        "print(\"Recall: \", recall)\n",
        "print(\"F1 score: \", f1)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "qXgPAKtPJiVI",
        "outputId": "b86823af-a9e7-4339-e2d9-4c13328a64a6"
      },
      "execution_count": 63,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Precision:  0.732781028240936\n",
            "Recall:  0.7115551694178974\n",
            "F1 score:  0.7094612321075684\n"
          ]
        }
      ]
    }
  ]
}