{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# {{ model_class }} Sequence Classification Tutorial\n",
    "\n",
    "Welcome to this in-depth tutorial on using the `{{ model_class }}` for sequence classification tasks, employing the Hugging Face Transformers library. In this notebook, you'll learn how to prepare your model and data for tasks like sentiment analysis, topic classification, and more."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Environment Setup\n",
    "\n",
    "Firstly, let's set up our working environment by importing necessary libraries and configuring the device settings."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Importing Transformers and torch libraries\n",
    "from transformers import {{ model_class }}, {{ tokenizer_class }}\n",
    "import torch\n",
    "\n",
    "# Checking GPU availability\n",
    "device = 'cuda' if torch.cuda.is_available() and {{ use_cuda }} else 'cpu'\n",
    "print(f'Using device: {device}')\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Model and Tokenizer Loading\n",
    "\n",
    "Let's proceed by loading the `{{ model_class }}` and its associated tokenizer, specifically the `{{ model_name }}` model for sequence classification."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Loading the model and tokenizer\n",
    "model = {{ model_class }}.from_pretrained('{{ model_name }}')\n",
    "tokenizer = {{ tokenizer_class }}.from_pretrained('{{ tokenizer_name }}')\n",
    "\n",
    "# Configuring the model for the current device\n",
    "model = model.to(device)\n",
    "\n",
    "# Model precision configuration\n",
    "if '{{ precision }}' == 'float16':\n",
    "    model = model.half()\n",
    "\n",
    "# Explanation of the configurations and their impact..."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Sequence Classification Demonstration\n",
    "\n",
    "Now, we will demonstrate how to apply the `{{ model_class }}` for sequence classification. We'll take an example sentence and classify its sentiment."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Example sentence for classification\n",
    "sentence = 'I love using transformers for natural language processing.'\n",
    "inputs = tokenizer(sentence, return_tensors='pt')\n",
    "inputs = inputs.to(device)\n",
    "\n",
    "# Performing sequence classification\n",
    "with torch.no_grad():\n",
    "    outputs = model(**inputs)\n",
    "    predictions = torch.argmax(outputs.logits, dim=-1)\n",
    "\n",
    "# Processing and displaying the results\n",
    "print(f'Sentence sentiment: {model.config.id2label[predictions.item()]}')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Tips for Effective Sequence Classification\n",
    "\n",
    "This section provides insights and best practices for enhancing model performance, handling various data formats, and troubleshooting common challenges in sequence classification."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Conclusion\n",
    "\n",
    "You have now explored the basic functionalities of `{{ model_class }}` for sequence classification. We encourage you to delve deeper into the Transformers library for more advanced use cases and features."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Fine-Tuning the Model for Sequence Classification\n",
    "\n",
    "In this section, we'll fine-tune `{{ model_class }}` for a specific sequence classification task. Fine-tuning involves training the model on a targeted dataset to improve its performance on that specific task. We'll use a dataset suitable for our classification task, such as sentiment analysis."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Preparing the Dataset\n",
    "\n",
    "Let's start by loading and preparing our dataset. For sentiment analysis, we can use a dataset like IMDb movie reviews."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Load and prepare the IMDb dataset\n",
    "from datasets import load_dataset\n",
    "dataset = load_dataset('imdb')\n",
    "\n",
    "# Preprocessing function for the dataset\n",
    "def preprocess_function(examples):\n",
    "    # Tokenize the text and truncate/pad it for uniformity\n",
    "    return tokenizer(examples['text'], truncation=True, padding='max_length')\n",
    "\n",
    "# Apply preprocessing\n",
    "dataset = dataset.map(preprocess_function, batched=True)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Configuring Training Parameters\n",
    "\n",
    "Next, we need to set up the training parameters, including the optimizer."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "from transformers import AdamW\n",
    "\n",
    "# Optimizer\n",
    "optimizer = AdamW(model.parameters(), lr=5e-5)\n",
    "\n",
    "# Training parameters\n",
    "num_epochs = 3\n",
    "batch_size = 16  # Adjust as per GPU capacity"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Training Loop\n",
    "\n",
    "Now, we will create the training loop, where the model will learn to classify the sentiment of the text."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "from tqdm.auto import tqdm\n",
    "\n",
    "model.train()\n",
    "for epoch in range(num_epochs):\n",
    "    progress_bar = tqdm(dataset['train'], desc=f'Epoch {epoch + 1}', leave=False)\n",
    "    for batch in progress_bar:\n",
    "        # Forward pass and loss calculation\n",
    "        outputs = model(**batch)\n",
    "        loss = outputs.loss\n",
    "        # Backward pass\n",
    "        loss.backward()\n",
    "        optimizer.step()\n",
    "        optimizer.zero_grad()\n",
    "        progress_bar.set_postfix({'loss': loss.item()})"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Evaluating the Fine-Tuned Model\n",
    "\n",
    "It's crucial to evaluate the model on a validation or test set to assess its performance after fine-tuning."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "model.eval()\n",
    "# Add code for evaluating the model on the validation/test set"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Conclusion\n",
    "\n",
    "This tutorial covered how to fine-tune `{{ model_class }}` for a sequence classification task. Experiment with different datasets, hyperparameters, and training strategies to optimize the model for your specific requirements."
   ]
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.8.5"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 4
}
