Search_Jam_ML_Model.ipynb

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    "accelerator": "GPU"
  },
  "cells": [
    {
      "cell_type": "markdown",
      "source": [
        "# Essential import/installs\n",
        "--------------------------------"
      ],
      "metadata": {
        "id": "uFwrdQOrAdLd"
      }
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "w3O31zOP_riI"
      },
      "outputs": [],
      "source": [
        "!pip install datasets\n",
        "!pip install tqdm boto3 requests regex sentencepiece sacremoses\n",
        "!pip install transformers\n",
        "!pip install transformers[sentencepiece]\n",
        "!pip install datasets\n",
        "!pip install -U accelerate\n",
        "!pip install -U transformers"
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "from huggingface_hub import notebook_login\n",
        "from collections import defaultdict\n",
        "import re\n",
        "import numpy as np\n",
        "import torch\n",
        "from transformers import AutoConfig, DataCollatorWithPadding, Trainer, TrainingArguments, BertTokenizer, BertConfig,BertForSequenceClassification, EarlyStoppingCallback\n",
        "from sklearn.model_selection import train_test_split\n",
        "from sklearn.metrics import accuracy_score, f1_score\n",
        "import pandas as pd\n",
        "from datasets import load_dataset, Dataset, DatasetDict\n",
        "import nltk\n",
        "from nltk.corpus import stopwords\n",
        "from nltk.tokenize import word_tokenize\n",
        "nltk.download(\"stopwords\")\n",
        "nltk.download('wordnet')\n",
        "nltk.download('punkt')"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "gikKyW-MC8KI",
        "outputId": "07b93cbc-047e-443f-97d3-30f9157acb61"
      },
      "execution_count": 121,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "[nltk_data] Downloading package stopwords to /root/nltk_data...\n",
            "[nltk_data]   Package stopwords is already up-to-date!\n",
            "[nltk_data] Downloading package wordnet to /root/nltk_data...\n",
            "[nltk_data]   Package wordnet is already up-to-date!\n",
            "[nltk_data] Downloading package punkt to /root/nltk_data...\n",
            "[nltk_data]   Package punkt is already up-to-date!\n"
          ]
        },
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "True"
            ]
          },
          "metadata": {},
          "execution_count": 121
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "device = torch.device('cuda' if torch.cuda.is_available() else 'cpu')"
      ],
      "metadata": {
        "id": "r_0q1X13IkjL"
      },
      "execution_count": 122,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": [
        "# Data preprocessing\n",
        "------------------------"
      ],
      "metadata": {
        "id": "WN3ASWBxC9Fn"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "# Initialize the stop words set\n",
        "words_to_keep = {\"but\",\"as\",\"until\",\"while\",\"against\",\"not\",\"only\",\"should\"}\n",
        "words_to_remove = {\"oh\", \"yeah\"}\n",
        "stop_words = set(stopwords.words('english'))\n",
        "stop_words.difference_update(words_to_keep)     # Remove the words that we want to keep\n",
        "stop_words.update(words_to_remove)              # Add the extra words that we want to remove\n",
        "\n",
        "lemmatizer = nltk.stem.WordNetLemmatizer()      # Create a lemmatizer to simplify the words\n",
        "\n",
        "def remove_special_characters(text):\n",
        "    # Use regex to remove all non alphabetical characters\n",
        "    cleaned_text = re.sub(r'[^a-zA-Z\\s]', '', text)\n",
        "    return cleaned_text\n",
        "\n",
        "def remove_stopwords(text, words_to_remove=None):\n",
        "    # Tokenize the input text\n",
        "    words = word_tokenize(text)\n",
        "\n",
        "    if words_to_remove != None:\n",
        "      stop_words.update(words_to_remove)        # Include teh words we want to remove\n",
        "\n",
        "    # Remove stop words from the tokenized words\n",
        "    filtered_words = [lemmatizer.lemmatize(remove_special_characters(word.lower())) for word in words if word.lower() not in stop_words]\n",
        "\n",
        "    # Reconstruct the text without stop words\n",
        "    filtered_text = ' '.join(filtered_words)\n",
        "\n",
        "    return filtered_text\n"
      ],
      "metadata": {
        "id": "XEqrGHmoZ5Z5"
      },
      "execution_count": 123,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "# Create a set with all negations in dataset to avoid misclassification of negative sentences\n",
        "negations = {\"t\",\n",
        "             \"dont\",\n",
        "             \"don\",\n",
        "             \"wasnt\",\n",
        "             \"wasn\",\n",
        "             \"havent\",\n",
        "             \"haven\",\n",
        "             \"hasnt\",\n",
        "             \"hasn\",\n",
        "             \"werent\",\n",
        "             \"weren\",\n",
        "             \"doesnt\",\n",
        "             \"doesn\",\n",
        "             \"didnt\",\n",
        "             \"didn\",\n",
        "             \"hadnt\",\n",
        "             \"hadn\",\n",
        "             \"shouldnt\",\n",
        "             \"shouldn\",\n",
        "             \"wouldnt\",\n",
        "             \"wouldn\",\n",
        "             \"arent\",\n",
        "             \"aren\",\n",
        "             \"isnt\",\n",
        "             \"isn\",\n",
        "             \"couldnt\",\n",
        "             \"couldn\"\n",
        "             }\n",
        "\n",
        "# Loading dataset\n",
        "dataset = load_dataset(\"dair-ai/emotion\")\n",
        "\n",
        "# Transforming all of it to Pandas\n",
        "train_dataframe = dataset['train'].to_pandas()\n",
        "val_dataframe = dataset['validation'].to_pandas()\n",
        "test_dataframe = dataset['test'].to_pandas()\n",
        "\n",
        "# Remove all the stop_words and non alphabetic characters\n",
        "train_dataframe['text'] = train_dataframe['text'].apply(remove_stopwords, words_to_remove=negations)\n",
        "val_dataframe['text'] = val_dataframe['text'].apply(remove_stopwords, words_to_remove=negations)\n",
        "test_dataframe['text'] = test_dataframe['text'].apply(remove_stopwords, words_to_remove=negations)\n",
        "\n",
        "# Create the dataset, to train and test the model\n",
        "model_data = DatasetDict({\n",
        "    'train': Dataset.from_pandas(pd.concat([train_dataframe['text'], train_dataframe['label']],axis=1)),\n",
        "    'validation': Dataset.from_pandas(pd.concat([val_dataframe['text'], val_dataframe['label']],axis=1)),\n",
        "    'test': Dataset.from_pandas(pd.concat([test_dataframe['text'], test_dataframe['label']],axis=1))\n",
        "})\n",
        "\n",
        "\n",
        "\n"
      ],
      "metadata": {
        "id": "GSB5a_ZdQT2s"
      },
      "execution_count": 124,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "# Name of the model to import\n",
        "model_ckpt = \"bert-base-uncased\"\n",
        "\n",
        "label2id = {\"sadness\": 0,\n",
        "            \"joy\": 1,\n",
        "            \"love\": 2,\n",
        "            \"anger\": 3,\n",
        "            \"fear\": 4,\n",
        "            \"surprise\": 5}\n",
        "id2label = {\"0\": \"sadness\",\n",
        "            \"1\": \"joy\",\n",
        "            \"2\": \"love\",\n",
        "            \"3\": \"anger\",\n",
        "            \"4\": \"fear\",\n",
        "            \"5\": \"surprise\"}\n",
        "\n",
        "unique_labels = ['sadness', 'joy','love', 'anger', 'fear', 'surprise']\n",
        "num_labels = len(unique_labels)"
      ],
      "metadata": {
        "id": "K51K4n6DAAwQ"
      },
      "execution_count": 126,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "config = BertConfig.from_pretrained(model_ckpt,\n",
        "                                    label2id=label2id,\n",
        "                                    id2label=id2label)"
      ],
      "metadata": {
        "id": "GVbd9bPmABTr"
      },
      "execution_count": 127,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": [
        "### Setting up the Model's Environment\n",
        "------------------------------------"
      ],
      "metadata": {
        "id": "fWCpp-tb_JXw"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "tokenizer = BertTokenizer.from_pretrained(model_ckpt)\n",
        "\n",
        "def tokenize_function(example):\n",
        "    return tokenizer(example['text'],truncation = True)\n",
        "\n",
        "# We tokenize the input\n",
        "tokenized_dataset = model_data.map(tokenize_function, batched = True, batch_size = None)\n",
        "\n",
        "data_collator = DataCollatorWithPadding(tokenizer = tokenizer, padding=True)"
      ],
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      "execution_count": 131,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "Map:   0%|          | 0/16000 [00:00<?, ? examples/s]"
            ],
            "application/vnd.jupyter.widget-view+json": {
              "version_major": 2,
              "version_minor": 0,
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          "output_type": "display_data",
          "data": {
            "text/plain": [
              "Map:   0%|          | 0/2000 [00:00<?, ? examples/s]"
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            "application/vnd.jupyter.widget-view+json": {
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        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "Map:   0%|          | 0/2000 [00:00<?, ? examples/s]"
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      ]
    },
    {
      "cell_type": "code",
      "source": [
        "def compute_metrics(pred):\n",
        "    labels = pred.label_ids\n",
        "    preds = pred.predictions.argmax(-1)\n",
        "    f1 = f1_score(labels, preds, average = 'weighted')\n",
        "    acc = accuracy_score(labels, preds)\n",
        "    return {'accuracy': acc, 'f1': f1}"
      ],
      "metadata": {
        "id": "WM92IUnIAmvL"
      },
      "execution_count": 132,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": [
        "# Creating, Training and Testing the model\n",
        "-------------------------------------"
      ],
      "metadata": {
        "id": "vsrCZe_rjntY"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "model = (BertForSequenceClassification.from_pretrained(model_ckpt, config=config).to(device))"
      ],
      "metadata": {
        "id": "IT2EDAjNAuuC"
      },
      "execution_count": 135,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "# Setting the Right Hyperparameters\n",
        "epochs = 8\n",
        "batch_size = 128\n",
        "lr = 5e-5\n",
        "\n",
        "# Initializing the Trainging Arguments\n",
        "logging_steps = len(tokenized_dataset['train']) // batch_size\n",
        "training_args = TrainingArguments(output_dir = './output',\n",
        "                                overwrite_output_dir = True,\n",
        "                                num_train_epochs = epochs,\n",
        "                                learning_rate = lr,\n",
        "                                optim = \"adamw_torch\",\n",
        "                                weight_decay = 0,\n",
        "                                evaluation_strategy = 'steps',\n",
        "                                per_device_train_batch_size = batch_size,\n",
        "                                per_device_eval_batch_size = batch_size,\n",
        "                                disable_tqdm = False,\n",
        "                                logging_strategy = 'steps',\n",
        "                                logging_steps = 125,\n",
        "                                log_level = 'error',\n",
        "                                report_to = 'none',\n",
        "                                push_to_hub = False,\n",
        "                                save_strategy = 'steps',\n",
        "                                eval_steps = 125,\n",
        "                                save_total_limit = 5,\n",
        "                                metric_for_best_model = 'accuracy',\n",
        "                                load_best_model_at_end = True\n",
        "                                )\n",
        "\n",
        "# Initializing the trainer\n",
        "trainer = Trainer(model = model,\n",
        "                tokenizer = tokenizer,\n",
        "                data_collator = data_collator,\n",
        "                args = training_args,\n",
        "                compute_metrics = compute_metrics,\n",
        "                train_dataset = tokenized_dataset[\"train\"],\n",
        "                eval_dataset = tokenized_dataset[\"validation\"],\n",
        "                callbacks = [EarlyStoppingCallback(early_stopping_patience=1)]  # Exits in case of accuracy lowering\n",
        "                )"
      ],
      "metadata": {
        "id": "FSshP3i4kkly"
      },
      "execution_count": 136,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "trainer.train()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 284
        },
        "id": "IAf8CKr5pvME",
        "outputId": "aa6b06db-763f-46ab-9618-ae2bc4c5c40d"
      },
      "execution_count": 137,
      "outputs": [
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          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<IPython.core.display.HTML object>"
            ],
            "text/html": [
              "\n",
              "    <div>\n",
              "      \n",
              "      <progress value='625' max='1000' style='width:300px; height:20px; vertical-align: middle;'></progress>\n",
              "      [ 625/1000 07:30 < 04:30, 1.38 it/s, Epoch 5/8]\n",
              "    </div>\n",
              "    <table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              " <tr style=\"text-align: left;\">\n",
              "      <th>Step</th>\n",
              "      <th>Training Loss</th>\n",
              "      <th>Validation Loss</th>\n",
              "      <th>Accuracy</th>\n",
              "      <th>F1</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <td>125</td>\n",
              "      <td>0.708600</td>\n",
              "      <td>0.213343</td>\n",
              "      <td>0.926000</td>\n",
              "      <td>0.926457</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>250</td>\n",
              "      <td>0.162900</td>\n",
              "      <td>0.134070</td>\n",
              "      <td>0.940000</td>\n",
              "      <td>0.940514</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>375</td>\n",
              "      <td>0.111800</td>\n",
              "      <td>0.141388</td>\n",
              "      <td>0.938000</td>\n",
              "      <td>0.938149</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>500</td>\n",
              "      <td>0.083700</td>\n",
              "      <td>0.123440</td>\n",
              "      <td>0.945500</td>\n",
              "      <td>0.944914</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>625</td>\n",
              "      <td>0.064100</td>\n",
              "      <td>0.150956</td>\n",
              "      <td>0.939500</td>\n",
              "      <td>0.939842</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table><p>"
            ]
          },
          "metadata": {}
        },
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "TrainOutput(global_step=625, training_loss=0.22619726409912108, metrics={'train_runtime': 451.0333, 'train_samples_per_second': 283.793, 'train_steps_per_second': 2.217, 'total_flos': 1432033347548160.0, 'train_loss': 0.22619726409912108, 'epoch': 5.0})"
            ]
          },
          "metadata": {},
          "execution_count": 137
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "train_predictions = trainer.predict(tokenized_dataset['train'])\n",
        "test_predictions = trainer.predict(tokenized_dataset['test'])\n",
        "validation_predictions = trainer.predict(tokenized_dataset['validation'])\n",
        "\n",
        "# Compute metrics on the predictions for each dataset\n",
        "train_metrics = compute_metrics(train_predictions)\n",
        "test_metrics = compute_metrics(test_predictions)\n",
        "validation_metrics = compute_metrics(validation_predictions)\n",
        "\n",
        "# Print the metrics for all three datasets\n",
        "print(\"Metrics on Train Data:\")\n",
        "print(train_metrics)\n",
        "print(\"Metrics on Test Data:\")\n",
        "print(test_metrics)\n",
        "print(\"Metrics on Validation Data:\")\n",
        "print(validation_metrics)"
      ],
      "metadata": {
        "id": "eXJjZ8AUma46",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 121
        },
        "outputId": "6dc5befe-c798-4070-8978-1f87f23ec39f"
      },
      "execution_count": 138,
      "outputs": [
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            "text/html": []
          },
          "metadata": {}
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Metrics on Train Data:\n",
            "{'accuracy': 0.97125, 'f1': 0.9707613472552109}\n",
            "Metrics on Test Data:\n",
            "{'accuracy': 0.933, 'f1': 0.9315542072364552}\n",
            "Metrics on Validation Data:\n",
            "{'accuracy': 0.9455, 'f1': 0.9449137204306589}\n"
          ]
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      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "# Saving the model\n",
        "---------------------"
      ],
      "metadata": {
        "id": "N9_uIDJD_4kE"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "notebook_login()"
      ],
      "metadata": {
        "id": "FX50K5HJmAYZ"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "trainer.push_to_hub(\"RicoCHEH/Bert_test\")"
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        "outputId": "3df1801c-9e4e-41c7-9c0e-bb052572a113"
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      "execution_count": 141,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "Upload 2 LFS files:   0%|          | 0/2 [00:00<?, ?it/s]"
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        {
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          "data": {
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              "model.safetensors:   0%|          | 0.00/438M [00:00<?, ?B/s]"
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              "training_args.bin:   0%|          | 0.00/4.54k [00:00<?, ?B/s]"
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        {
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          "metadata": {},
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}