{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# {{ model_class }} Sequence-to-Sequence Tutorial\n",
    "\n",
    "Welcome to this comprehensive tutorial on using `{{ model_class }}` for sequence-to-sequence tasks, leveraging the Hugging Face Transformers library. This notebook will guide you through setting up the model, preparing your data, and using the model for tasks such as translation, summarization, and more."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Environment Setup\n",
    "\n",
    "Let's start by setting up our environment and importing the necessary libraries."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Importing the Transformers library and torch\n",
    "from transformers import {{ model_class }}, {{ tokenizer_class }}\n",
    "import torch\n",
    "\n",
    "# Checking for 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",
    "Next, we'll load the `{{ model_class }}` and its corresponding tokenizer, using the `{{ model_name }}` model for our sequence-to-sequence tasks."
   ]
  },
  {
   "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",
    "# Setting up the model on the right device\n",
    "model = model.to(device)\n",
    "\n",
    "# Configuring model precision if necessary\n",
    "if '{{ precision }}' == 'float16':\n",
    "    model = model.half()\n",
    "\n",
    "# Explaining each configuration and its impact..."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Sequence-to-Sequence Task Demonstration\n",
    "\n",
    "In this section, we will demonstrate how to use the `{{ model_class }}` for a sequence-to-sequence task. We will provide a source sequence, and the model will generate a corresponding target sequence."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Defining a source sequence for demonstration\n",
    "source_sequence = '...'  # Add your source sequence here\n",
    "inputs = tokenizer(source_sequence, return_tensors='pt')\n",
    "inputs = inputs.to(device)\n",
    "\n",
    "# Generating the target sequence\n",
    "with torch.no_grad():\n",
    "    outputs = model.generate(**inputs)\n",
    "    target_sequence = tokenizer.decode(outputs[0], skip_special_tokens=True)\n",
    "\n",
    "# Displaying the target sequence\n",
    "print('Generated Target Sequence:\\n', target_sequence)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Additional Tips and Best Practices\n",
    "\n",
    "This section includes tips on optimizing model performance, handling different types of sequences, and troubleshooting common issues in sequence-to-sequence tasks."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Conclusion\n",
    "\n",
    "You have now learned the basics of using `{{ model_class }}` for sequence-to-sequence tasks. Don't forget to explore the Transformers library further for advanced features and applications."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Fine-Tuning the Model for Sequence-to-Sequence Tasks\n",
    "\n",
    "In this section, we'll fine-tune `{{ model_class }}` for a specific sequence-to-sequence task. Fine-tuning involves adapting the pre-trained model to a specific task with additional training. We'll choose a suitable dataset for a task like translation or summarization."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Preparing the Dataset\n",
    "\n",
    "We'll start by preparing our dataset for the sequence-to-sequence task. For this example, let's assume we're working on a translation task."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Load and prepare a translation dataset\n",
    "from datasets import load_dataset\n",
    "dataset = load_dataset('translation_dataset_name')\n",
    "\n",
    "# Preprocessing function for the dataset\n",
    "def preprocess_function(examples):\n",
    "    # Tokenize source and target texts\n",
    "    # Truncate or pad based on model's max length\n",
    "    return tokenizer(examples['source_language'], examples['target_language'], truncation=True, padding='max_length')\n",
    "\n",
    "# Apply the preprocessing\n",
    "dataset = dataset.map(preprocess_function, batched=True)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Setting Up Training Parameters\n",
    "\n",
    "Next, we'll define the optimizer and training parameters for fine-tuning."
   ]
  },
  {
   "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 = 8  # Adjust based on GPU memory"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Training Loop\n",
    "\n",
    "Now we'll implement the training loop, where the model will learn to translate the source text to the target 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",
    "After fine-tuning, we should evaluate the model's performance on a validation or test set to ensure it has learned the task effectively."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "model.eval()\n",
    "# Add evaluation code for the sequence-to-sequence task"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Conclusion\n",
    "\n",
    "You have now learned how to fine-tune `{{ model_class }}` for a sequence-to-sequence task. Experiment with different datasets and hyperparameters to optimize the model for your specific use case."
   ]
  }
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