DL_Project_Model_roberta_facebook.ipynb

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  "cells": [
    {
      "cell_type": "code",
      "execution_count": 1,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "hxpmMU-UZuAA",
        "outputId": "3fce7afe-1c09-491e-ed0b-2a2d3953b73c"
      },
      "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[90m╺\u001b[0m\u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m153.6/507.1 kB\u001b[0m \u001b[31m4.6 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[31m7.9 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[31m6.4 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[31m7.4 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[31m10.0 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[31m8.6 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[31m10.6 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[31m5.8 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": "c8495f89-cd09-4e28-e77f-c004fa168b60"
      },
      "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, 7.69MB/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"
      ],
      "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\""
      ],
      "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": 237,
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        "outputId": "e382740a-35a4-4da1-db82-9a23ff98b487"
      },
      "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": [
              "config.json:   0%|          | 0.00/615 [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]"
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          },
          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "tokenizer.json:   0%|          | 0.00/9.10M [00:00<?, ?B/s]"
            ],
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          },
          "metadata": {}
        }
      ]
    },
    {
      "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": "b46b3d20-9b2a-48d4-f894-0ccb5a89b4d1"
      },
      "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": "91804f4b4b794c87a7c21b9da7ba7aaa"
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          },
          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "Generating test split: 0 examples [00:00, ? examples/s]"
            ],
            "application/vnd.jupyter.widget-view+json": {
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        }
      ]
    },
    {
      "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)"
      ],
      "metadata": {
        "id": "UyJQW23tdZIy",
        "colab": {
          "base_uri": "https://localhost:8080/",
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      },
      "execution_count": 11,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "Map:   0%|          | 0/6125 [00:00<?, ? examples/s]"
            ],
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        {
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              "Map:   0%|          | 0/1151 [00:00<?, ? examples/s]"
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    },
    {
      "cell_type": "code",
      "source": [
        "tokenized_dataset[\"train\"][2]"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "mJEVeX-CsKuj",
        "outputId": "9ba66602-c814-4fbe-afdc-6b4b2715ef0f"
      },
      "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": {},
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        }
      ]
    },
    {
      "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": [
            "0b9870e7da5d4a80a356ca4ef74a8803",
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          ]
        },
        "id": "cETafsH_plPO",
        "outputId": "9b67115d-0af4-4e33-d817-2c60cdbcfe89"
      },
      "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,
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          },
          "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": 104,
          "referenced_widgets": [
            "bf3375629aa043e083c84475ba7bbb42",
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            "a1d366285ced4d60a99f99bcb15178e6"
          ]
        },
        "id": "Tb4vzdxUe8vk",
        "outputId": "3815a5bc-47e1-414e-ff19-8ad43eba4943"
      },
      "execution_count": 16,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "model.safetensors:   0%|          | 0.00/1.12G [00:00<?, ?B/s]"
            ],
            "application/vnd.jupyter.widget-view+json": {
              "version_major": 2,
              "version_minor": 0,
              "model_id": "bf3375629aa043e083c84475ba7bbb42"
            }
          },
          "metadata": {}
        },
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "Some weights of XLMRobertaForSequenceClassification were not initialized from the model checkpoint at FacebookAI/xlm-roberta-base and are newly initialized: ['classifier.out_proj.bias', 'classifier.dense.weight', 'classifier.out_proj.weight', 'classifier.dense.bias']\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": 49,
          "referenced_widgets": [
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            "244fed1cb29a49839dedf6ba02d89190"
          ]
        },
        "outputId": "4d3c508f-b1fd-485d-87ae-89786ecc5c7e"
      },
      "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": "41dd2e84a01b4f0983ad9b2cf77279d2"
            }
          },
          "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=1e-5,\n",
        "    per_device_train_batch_size=16,\n",
        "    per_device_eval_batch_size=16,\n",
        "    num_train_epochs=5,\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": 304
        },
        "id": "4Jut8nzQgsMc",
        "outputId": "24b1c690-82cf-4d22-fa75-bbfe929de718"
      },
      "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='1915' max='1915' style='width:300px; height:20px; vertical-align: middle;'></progress>\n",
              "      [1915/1915 10:45, Epoch 5/5]\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>1.496247</td>\n",
              "      <td>0.421373</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>2</td>\n",
              "      <td>1.652100</td>\n",
              "      <td>1.225842</td>\n",
              "      <td>0.576890</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>3</td>\n",
              "      <td>1.095400</td>\n",
              "      <td>1.178461</td>\n",
              "      <td>0.612511</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>4</td>\n",
              "      <td>0.918900</td>\n",
              "      <td>1.195764</td>\n",
              "      <td>0.605560</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>5</td>\n",
              "      <td>0.918900</td>\n",
              "      <td>1.172159</td>\n",
              "      <td>0.631625</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table><p>"
            ]
          },
          "metadata": {}
        },
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "TrainOutput(global_step=1915, training_loss=1.1381027978643115, metrics={'train_runtime': 647.2247, 'train_samples_per_second': 47.317, 'train_steps_per_second': 2.959, 'total_flos': 786927520312500.0, 'train_loss': 1.1381027978643115, 'epoch': 5.0})"
            ]
          },
          "metadata": {},
          "execution_count": 20
        }
      ]
    },
    {
      "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)\n"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "V5a8-Kyqjwbb",
        "outputId": "12a50fba-207c-43b3-968a-6adda8f82d52"
      },
      "execution_count": 21,
      "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": 22,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "tokenizer(text)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "l72Z6ZGVBPoM",
        "outputId": "7d25bf2b-f8a3-4671-edfd-739636cad8f4"
      },
      "execution_count": 23,
      "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": 23
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "predict(text)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "C1DYrMSCkxtY",
        "outputId": "625cb4df-f81c-4e91-874d-468cb6eb1144"
      },
      "execution_count": 24,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "[[{'label': 'SAD', 'score': 0.5409653782844543},\n",
              "  {'label': 'HATE', 'score': 0.020761720836162567},\n",
              "  {'label': 'OTHER', 'score': 0.026956496760249138},\n",
              "  {'label': 'FEAR', 'score': 0.007126557640731335},\n",
              "  {'label': 'ANGRY', 'score': 0.005131038837134838},\n",
              "  {'label': 'HAPPY', 'score': 0.39064082503318787},\n",
              "  {'label': 'SURPRISE', 'score': 0.008417924866080284}]]"
            ]
          },
          "metadata": {},
          "execution_count": 24
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "history=pd.DataFrame(trainer.state.log_history)"
      ],
      "metadata": {
        "id": "syrYWIIAmMPz"
      },
      "execution_count": 25,
      "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": 26,
      "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": "298d2f94-0080-41eb-9a00-cab37cc563cb"
      },
      "execution_count": 27,
      "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": "90cd2a5e-e070-49e6-835f-bbf93ea01ea3"
      },
      "execution_count": 28,
      "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(\"alpha\")\n",
        "plt.ylabel(\"Accuracy\")\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": "f6481d26-b8fe-4489-dcff-3632b088c49d"
      },
      "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": {}
        }
      ]
    }
  ]
}