DL_Project_Model_roberta-base-ft-udpos28.ipynb

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  "cells": [
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      "cell_type": "code",
      "execution_count": 16,
      "metadata": {
        "id": "hxpmMU-UZuAA"
      },
      "outputs": [],
      "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": "f135aa54-baf0-4093-94b2-55eaec466378"
      },
      "execution_count": 17,
      "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, 53.8MB/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": 18,
      "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\""
      ],
      "metadata": {
        "id": "7HdyAr5HuVMK"
      },
      "execution_count": 19,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "tokenizer = AutoTokenizer.from_pretrained(MODEL_NAME)"
      ],
      "metadata": {
        "id": "s7Y1YTcUdEwO"
      },
      "execution_count": 20,
      "outputs": []
    },
    {
      "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": 21,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "TRAIN_FILE=\"dataset/train.csv\"\n",
        "TEST_FILE=\"dataset/test.csv\""
      ],
      "metadata": {
        "id": "NhGLpwbdoTWK"
      },
      "execution_count": 22,
      "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": 23,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "dataset = load_dataset('csv', data_files={'train': TRAIN_FILE,'test': TEST_FILE})"
      ],
      "metadata": {
        "id": "fXvK7jK5oGHK"
      },
      "execution_count": 24,
      "outputs": []
    },
    {
      "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": 25,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "tokenized_dataset = dataset.map(preprocess_function, batched=True)"
      ],
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      "execution_count": 26,
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        {
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              "Map:   0%|          | 0/1151 [00:00<?, ? examples/s]"
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        "tokenized_dataset[\"train\"][2]"
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      "execution_count": 27,
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              "  1,\n",
              "  1,\n",
              "  1,\n",
              "  1,\n",
              "  1,\n",
              "  1,\n",
              "  1,\n",
              "  1,\n",
              "  1,\n",
              "  1,\n",
              "  1,\n",
              "  1,\n",
              "  1,\n",
              "  1,\n",
              "  1,\n",
              "  1,\n",
              "  1,\n",
              "  1,\n",
              "  1,\n",
              "  1,\n",
              "  1,\n",
              "  1,\n",
              "  1,\n",
              "  1,\n",
              "  1,\n",
              "  1,\n",
              "  1,\n",
              "  1]}"
            ]
          },
          "metadata": {},
          "execution_count": 27
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "data_collator = DataCollatorWithPadding(tokenizer=tokenizer)"
      ],
      "metadata": {
        "id": "WgBxUHNkeBWg"
      },
      "execution_count": 28,
      "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": {
        "id": "cETafsH_plPO"
      },
      "execution_count": 29,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "# model = AutoModelForSequenceClassification.from_pretrained(\n",
        "#     MODEL_NAME, config=config\n",
        "# )"
      ],
      "metadata": {
        "id": "mWyyxcDkvrKU"
      },
      "execution_count": 30,
      "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": [
            "26f6397d74ef4de49c8e1917b6911681",
            "a250c95dc356449894df0e418fbe1446",
            "44a5dc88cb344a84a35677c851092bb5",
            "4e376364b200443bb44c1c5a7863f154",
            "12f373633ae34ef78e4eb83e46437c29",
            "1cd51839d08c430794470b200334d3a3",
            "343e5cbbf7dd48a1be796803e5b61038",
            "2a11ab5b654e4ca0a6a4339f2ca10daa",
            "a1b06f880df14556822c50da7e15e12e",
            "5b48c3d8ae7a4e6eb0c961fee7cbbc63",
            "f2b67e81373943c4adbe70c66bc68ae2"
          ]
        },
        "id": "Tb4vzdxUe8vk",
        "outputId": "4085244c-737a-446c-aacc-2a58ddbd478c"
      },
      "execution_count": 31,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "config.json:   0%|          | 0.00/1.37k [00:00<?, ?B/s]"
            ],
            "application/vnd.jupyter.widget-view+json": {
              "version_major": 2,
              "version_minor": 0,
              "model_id": "26f6397d74ef4de49c8e1917b6911681"
            }
          },
          "metadata": {}
        },
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "Some weights of XLMRobertaForSequenceClassification were not initialized from the model checkpoint at wietsedv/xlm-roberta-base-ft-udpos28-fa 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": 49,
          "referenced_widgets": [
            "66e1f55c8a284bd1b47bf1600c4cfc80",
            "22233edc62e745edb5e134c8c95dd4e5",
            "304cdf4228d14827b6fda73246a68a70",
            "a730866320f94cdf86760a0f63402e48",
            "b3264a3e46a640589bbf71e4b9966431",
            "04f0184a19024a6e96181797399135c5",
            "64976f0d9f18463fbd9f665f8c55f7cb",
            "ac92a5f05e9b4e938e48d634d0576428",
            "4bbe02639834451e8c151995f32630d6",
            "eef48b5bc0664c5fb9b75670bd34c0af",
            "380bb2dab8e9407daa3c8c7f6b280edf"
          ]
        },
        "outputId": "071f36ff-52c8-4cf9-f727-411f7305f556"
      },
      "execution_count": 32,
      "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": "66e1f55c8a284bd1b47bf1600c4cfc80"
            }
          },
          "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=2e-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": 33,
      "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": 34,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "trainer.train()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 304
        },
        "id": "4Jut8nzQgsMc",
        "outputId": "958b95a8-4ee6-4a9f-f001-df39faf7cdd7"
      },
      "execution_count": 35,
      "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 10:39, 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>1.505347</td>\n",
              "      <td>0.452650</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>2</td>\n",
              "      <td>1.602200</td>\n",
              "      <td>1.278498</td>\n",
              "      <td>0.571677</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>3</td>\n",
              "      <td>1.024100</td>\n",
              "      <td>1.211697</td>\n",
              "      <td>0.600348</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>4</td>\n",
              "      <td>0.818100</td>\n",
              "      <td>1.171851</td>\n",
              "      <td>0.621199</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>5</td>\n",
              "      <td>0.818100</td>\n",
              "      <td>1.189166</td>\n",
              "      <td>0.620330</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table><p>"
            ]
          },
          "metadata": {}
        },
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "TrainOutput(global_step=1915, training_loss=1.047796009352562, metrics={'train_runtime': 642.2851, 'train_samples_per_second': 47.681, 'train_steps_per_second': 2.982, 'total_flos': 786927520312500.0, 'train_loss': 1.047796009352562, 'epoch': 5.0})"
            ]
          },
          "metadata": {},
          "execution_count": 35
        }
      ]
    },
    {
      "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": "654b581b-46c5-4c25-fd0c-627bf77b9f28"
      },
      "execution_count": 36,
      "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": 37,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "tokenizer(text)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "l72Z6ZGVBPoM",
        "outputId": "66fde11c-c251-479a-e0a5-7d304abd5617"
      },
      "execution_count": 38,
      "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": 38
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "predict(text)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "C1DYrMSCkxtY",
        "outputId": "f388877d-b5f7-4860-d8bb-535f60f1812f"
      },
      "execution_count": 39,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "[[{'label': 'SAD', 'score': 0.49250268936157227},\n",
              "  {'label': 'HATE', 'score': 0.010366277769207954},\n",
              "  {'label': 'OTHER', 'score': 0.012455513700842857},\n",
              "  {'label': 'FEAR', 'score': 0.009040692821145058},\n",
              "  {'label': 'ANGRY', 'score': 0.004684250336140394},\n",
              "  {'label': 'HAPPY', 'score': 0.45539358258247375},\n",
              "  {'label': 'SURPRISE', 'score': 0.015557033009827137}]]"
            ]
          },
          "metadata": {},
          "execution_count": 39
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "history=pd.DataFrame(trainer.state.log_history)"
      ],
      "metadata": {
        "id": "syrYWIIAmMPz"
      },
      "execution_count": 40,
      "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": 41,
      "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": "ab1f066a-279e-434d-f8ba-a328f55f1a63"
      },
      "execution_count": 42,
      "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": "f2e1e272-8a08-4a9d-db8e-972da0cd510a"
      },
      "execution_count": 43,
      "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": "913ee56f-2725-48f4-ede2-824cae4375e8"
      },
      "execution_count": 44,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 640x480 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        }
      ]
    }
  ]
}