Sentiment_Analysis_with_Pytorch.ipynb

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  "cells": [
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "view-in-github",
        "colab_type": "text"
      },
      "source": [
        "<a href=\"https://colab.research.google.com/github/alturkim/nlp-notebooks/blob/main/Sentiment_Analysis_with_Pytorch.ipynb\" target=\"_parent\"><img src=\"https://colab.research.google.com/assets/colab-badge.svg\" alt=\"Open In Colab\"/></a>"
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "!pip install datasets evaluate transformers[sentencepiece]"
      ],
      "metadata": {
        "id": "9unczTpUG8w7"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "0GlhvMrFGsyE"
      },
      "outputs": [],
      "source": [
        "from transformers import AutoTokenizer, DataCollatorWithPadding\n",
        "from transformers import AutoModel, AdamW, get_scheduler\n",
        "from transformers import TrainingArguments, Trainer\n",
        "from datasets import load_dataset, arrow_dataset\n",
        "from datasets.arrow_dataset import Dataset\n",
        "\n",
        "import evaluate\n",
        "import torch\n",
        "from torch import nn\n",
        "from torch.optim import AdamW\n",
        "from torch.utils.data import DataLoader\n",
        "\n",
        "import numpy as np\n",
        "\n",
        "from tqdm.auto import tqdm\n",
        "\n",
        "from typing import Tuple, Dict"
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "class Classifier(nn.Module):\n",
        "    def __init__(self):\n",
        "        super(Classifier, self).__init__()\n",
        "        self.linear = nn.Linear(768, 768)\n",
        "        self.dropout = nn.Dropout(0.2)\n",
        "        self.classifier = nn.Linear(768, 2)\n",
        "        self.criterion = nn.CrossEntropyLoss()\n",
        "    def forward(self, features: torch.Tensor, labels: torch.Tensor):\n",
        "        output = self.linear(features)\n",
        "        output = nn.ReLU()(output)\n",
        "        output = self.dropout(output)\n",
        "        logits = self.classifier(output)\n",
        "\n",
        "        loss = self.criterion(logits, labels)\n",
        "        return loss, logits\n"
      ],
      "metadata": {
        "id": "ryA8DFIbG6oz"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "def compute_metrics(eval_preds):\n",
        "    # eval_preds is an EvalPrediction object which is a named tuple\n",
        "    logits, labels = eval_preds\n",
        "    predictions = torch.argmax(logits, axis=-1)\n",
        "\n",
        "    metric_names = [\"precision\", \"recall\", \"f1\", \"accuracy\"]\n",
        "    results = dict()\n",
        "    for m in metric_names:\n",
        "        metric = evaluate.load(m)\n",
        "        results = {**results, **metric.compute(predictions=predictions, references=labels)}\n",
        "    return results\n",
        "\n",
        "class Trainer:\n",
        "    def __init__(self, model: Classifier, embed_column: str, \n",
        "                 train_dataloader: DataLoader, eval_dataloader: DataLoader,\n",
        "                 num_train_epochs: int, lr: float, device: torch.device):\n",
        "        self.model = model\n",
        "        self.embed_column = embed_column\n",
        "        self.train_dataloader = train_dataloader\n",
        "        self.eval_dataloader = eval_dataloader\n",
        "        self.num_train_epochs = num_train_epochs\n",
        "        self.lr = lr\n",
        "        self.device = device\n",
        "        self.optimizer = AdamW(self.model.parameters(), lr=self.lr)\n",
        "\n",
        "        self.model.to(self.device)\n",
        "\n",
        "    def train(self) -> None:\n",
        "        num_training_steps = self.num_train_epochs * len(self.train_dataloader)\n",
        "        progress_bar = tqdm(range(num_training_steps))\n",
        "        lr_scheduler = get_scheduler(\n",
        "            \"linear\",\n",
        "            optimizer=self.optimizer,\n",
        "            num_warmup_steps=0,\n",
        "            num_training_steps=num_training_steps,\n",
        "        )\n",
        "        for epoch in range(self.num_train_epochs):\n",
        "            self.model.train()\n",
        "            for batch in self.train_dataloader:\n",
        "                batch = {k: v.to(device) for k, v in batch.items()}\n",
        "                \n",
        "                loss, logits = self.model(batch[self.embed_column], batch[\"labels\"])\n",
        "                loss.backward()\n",
        "                self.optimizer.step()\n",
        "                lr_scheduler.step()\n",
        "                self.optimizer.zero_grad()\n",
        "                progress_bar.update(1)\n",
        "\n",
        "            # evaluate after each epoch\n",
        "            print(f\"evaluate ... epoch:{epoch}\")\n",
        "            results = self.evaluation()\n",
        "            print(results)\n",
        "\n",
        "    def evaluation(self) -> dict:\n",
        "        self.model.eval()\n",
        "        for batch in self.eval_dataloader:\n",
        "            batch = {k: v.to(device) for k, v in batch.items()}\n",
        "            with torch.no_grad():\n",
        "                _, logits = model(features=batch[self.embed_column], labels=batch[\"labels\"])\n",
        "            \n",
        "            labels = batch[\"labels\"]\n",
        "            return compute_metrics((logits, labels))\n",
        "        \n",
        "    def predict(self, dataset) -> dict:\n",
        "        self.model.eval()\n",
        "        dataloader = DataLoader(\n",
        "            dataset, \n",
        "            shuffle=True, \n",
        "            batch_size=8)\n",
        "        for batch in dataloader:\n",
        "            batch = {k: v.to(device) for k, v in batch.items()}\n",
        "            with torch.no_grad():\n",
        "                _, logits = model(features=batch[self.embed_column], labels=batch[\"labels\"])\n",
        "            \n",
        "            labels = batch[\"labels\"]\n",
        "            return compute_metrics((logits, labels))"
      ],
      "metadata": {
        "id": "1xwjDAilKk1T"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "def tokenize_and_embed(batch: arrow_dataset.Dataset):\n",
        "    processed = tokenizer(batch[\"text\"], truncation=True,\n",
        "                          padding=\"max_length\", return_tensors=\"pt\")\n",
        "    with torch.no_grad():\n",
        "        embeddings = lm(input_ids=processed[\"input_ids\"].to(device),\n",
        "                        attention_mask=processed[\"attention_mask\"].to(device))\n",
        "    cls_embd = embeddings.last_hidden_state[:, 0]\n",
        "    processed[\"cls_embed\"] = cls_embd\n",
        "\n",
        "    processed.pop(\"input_ids\")\n",
        "    processed.pop(\"attention_mask\")\n",
        "    return processed"
      ],
      "metadata": {
        "id": "kFE1Iw4o3pyM"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "def train_eval_test_split(dataset : Dataset) -> Dict[str, Dataset]:\n",
        "    split_datasets = dict()\n",
        "    train_eval_test = dataset.train_test_split(test_size=0.4, stratify_by_column=\"labels\", seed=10)\n",
        "    split_datasets[\"train\"] = train_eval_test[\"train\"]\n",
        "    eval_test = train_eval_test[\"test\"].train_test_split(test_size=0.5, stratify_by_column=\"labels\", seed=10)\n",
        "    split_datasets[\"eval\"] = eval_test[\"train\"]\n",
        "    split_datasets[\"test\"] = eval_test[\"test\"]\n",
        "    return split_datasets"
      ],
      "metadata": {
        "id": "hIW4nTyFqDan"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [],
      "metadata": {
        "id": "goMQZAv-Rqji"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "def get_stat(dataset : Dataset) -> None:\n",
        "    labels = dataset[\"labels\"]\n",
        "    pos_count = sum([1 for i in labels if i==1])\n",
        "    neg_count = sum([1 for i in labels if i==0])\n",
        "\n",
        "    pos_pct = pos_count/(pos_count + neg_count)\n",
        "    neg_pct = neg_count/(pos_count + neg_count)\n",
        "    print(f\"There are: \\n{pos_count} positive reviews.\\n{neg_count} negative reviews.\")\n",
        "    print(f\"Percentage of positive reviews: {pos_pct*100:.2f}%\")\n",
        "    print(f\"Percentage of negative reviews: {neg_pct*100:.2f}%\")"
      ],
      "metadata": {
        "id": "cgisFOcq0dM2"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "if __name__ == \"__main__\":\n",
        "    lm_checkpoint = \"distilbert-base-multilingual-cased\"\n",
        "    device = torch.device(\"cuda\") if torch.cuda.is_available() else torch.device(\"cpu\")\n",
        "    raw_dataset = load_dataset(\"ar_res_reviews\", split=\"train\").rename_column(\"polarity\", \"labels\")\n",
        "\n",
        "    split_datasets = train_eval_test_split(raw_dataset)\n",
        "\n",
        "    for split, data in split_datasets.items():\n",
        "        print(f\"{split} data stats\")\n",
        "        get_stat(data)\n",
        "        print()\n",
        "\n",
        "    tokenizer = AutoTokenizer.from_pretrained(lm_checkpoint)\n",
        "    lm = AutoModel.from_pretrained(lm_checkpoint)\n",
        "\n",
        "    lm.to(device)\n",
        "\n",
        "    tokenized_datasets = dict()\n",
        "    for split in [\"train\", \"eval\", \"test\"]:\n",
        "        tokenized_datasets[split] = split_datasets[split].map(tokenize_and_embed, batched=True, batch_size=8)\n",
        "        tokenized_datasets[split] = tokenized_datasets[split].remove_columns([\"text\", \"restaurant_id\", \"user_id\"])\n",
        "        tokenized_datasets[split] = tokenized_datasets[split].with_format(\"torch\")\n",
        "\n",
        "    train_dataloader = DataLoader(\n",
        "        tokenized_datasets[\"train\"], \n",
        "        shuffle=True,\n",
        "        batch_size=8)\n",
        "    \n",
        "    eval_dataloader = DataLoader(\n",
        "        tokenized_datasets[\"eval\"], \n",
        "        shuffle=True, \n",
        "        batch_size=8)\n"
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      "outputs": [
        {
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            "Downloading and preparing dataset ar_res_reviews/default to /root/.cache/huggingface/datasets/ar_res_reviews/default/0.0.0/f303714dc96c8056d45dca8950e5b7fe6ad59b88d0c095e07724e0484824031c...\n"
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        {
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          "data": {
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            ],
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          "metadata": {}
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Dataset ar_res_reviews downloaded and prepared to /root/.cache/huggingface/datasets/ar_res_reviews/default/0.0.0/f303714dc96c8056d45dca8950e5b7fe6ad59b88d0c095e07724e0484824031c. Subsequent calls will reuse this data.\n",
            "train data stats\n",
            "There are: \n",
            "3567 positive reviews.\n",
            "1451 negative reviews.\n",
            "Percentage of positive reviews: 71.08%\n",
            "Percentage of negative reviews: 28.92%\n",
            "\n",
            "eval data stats\n",
            "There are: \n",
            "1190 positive reviews.\n",
            "483 negative reviews.\n",
            "Percentage of positive reviews: 71.13%\n",
            "Percentage of negative reviews: 28.87%\n",
            "\n",
            "test data stats\n",
            "There are: \n",
            "1189 positive reviews.\n",
            "484 negative reviews.\n",
            "Percentage of positive reviews: 71.07%\n",
            "Percentage of negative reviews: 28.93%\n",
            "\n"
          ]
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        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "Some weights of the model checkpoint at distilbert-base-multilingual-cased were not used when initializing DistilBertModel: ['vocab_projector.bias', 'vocab_projector.weight', 'vocab_transform.bias', 'vocab_layer_norm.bias', 'vocab_layer_norm.weight', 'vocab_transform.weight']\n",
            "- This IS expected if you are initializing DistilBertModel from the checkpoint of a model trained on another task or with another architecture (e.g. initializing a BertForSequenceClassification model from a BertForPreTraining model).\n",
            "- This IS NOT expected if you are initializing DistilBertModel from the checkpoint of a model that you expect to be exactly identical (initializing a BertForSequenceClassification model from a BertForSequenceClassification model).\n"
          ]
        },
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    },
    {
      "cell_type": "code",
      "source": [
        "if __name__ == \"__main__\":\n",
        "    model = Classifier()\n",
        "    trainer = Trainer(model=model, \n",
        "                      embed_column=\"cls_embed\", \n",
        "                      train_dataloader=train_dataloader, \n",
        "                      eval_dataloader=eval_dataloader,\n",
        "                      num_train_epochs=5, \n",
        "                      lr=5e-5,\n",
        "                      device=device)\n",
        "    print(\"Evaluating before training ... \")\n",
        "    result = trainer.predict(tokenized_datasets[\"test\"])\n",
        "    print(result)\n",
        "    \n",
        "    print(\"training ... \")\n",
        "    trainer.train()\n",
        "\n",
        "    print(\"Evaluating after training ... \")\n",
        "    result = trainer.predict(tokenized_datasets[\"test\"])\n",
        "    print(result)\n",
        "    \n",
        "\n"
      ],
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      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Evaluating before training ... \n"
          ]
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "Downloading builder script:   0%|          | 0.00/7.55k [00:00<?, ?B/s]"
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          "data": {
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          "text": [
            "{'precision': 0.875, 'recall': 1.0, 'f1': 0.9333333333333333, 'accuracy': 0.875}\n",
            "training ... \n"
          ]
        },
        {
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          "output_type": "stream",
          "name": "stdout",
          "text": [
            "evaluate ... epoch:0\n",
            "{'precision': 0.625, 'recall': 1.0, 'f1': 0.7692307692307693, 'accuracy': 0.625}\n",
            "evaluate ... epoch:1\n",
            "{'precision': 0.75, 'recall': 1.0, 'f1': 0.8571428571428571, 'accuracy': 0.75}\n",
            "evaluate ... epoch:2\n",
            "{'precision': 0.75, 'recall': 1.0, 'f1': 0.8571428571428571, 'accuracy': 0.75}\n",
            "evaluate ... epoch:3\n",
            "{'precision': 0.8571428571428571, 'recall': 1.0, 'f1': 0.923076923076923, 'accuracy': 0.875}\n",
            "evaluate ... epoch:4\n",
            "{'precision': 0.75, 'recall': 1.0, 'f1': 0.8571428571428571, 'accuracy': 0.75}\n",
            "Evaluating after training ... \n",
            "{'precision': 0.875, 'recall': 1.0, 'f1': 0.9333333333333333, 'accuracy': 0.875}\n"
          ]
        }
      ]
    }
  ]
}