{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# {{ model_class }} Token Classification Tutorial\n",
    "\n",
    "Welcome to this in-depth tutorial on using `{{ model_class }}` for token classification tasks, with the Hugging Face Transformers library. This notebook will guide you through the process of preparing your model and data for tasks such as named entity recognition, part-of-speech tagging, and more."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Environment Setup\n",
    "\n",
    "Let's begin by setting up our environment, importing necessary libraries, and configuring our computation device."
   ]
  },
  {
   "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",
    "Now, let's load the `{{ model_class }}` and its corresponding tokenizer, using the `{{ model_name }}` model for token classification 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",
    "# 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": [
    "## Token Classification Demonstration\n",
    "\n",
    "In this section, we'll demonstrate how to use `{{ model_class }}` for token classification. We'll take an example sentence and classify each token."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Example sentence for token classification\n",
    "sentence = 'Hugging Face is a technology company based in New York.'\n",
    "inputs = tokenizer(sentence, return_tensors='pt')\n",
    "inputs = inputs.to(device)\n",
    "\n",
    "# Performing token classification\n",
    "with torch.no_grad():\n",
    "    outputs = model(**inputs)\n",
    "    predictions = torch.argmax(outputs.logits, dim=-1)\n",
    "\n",
    "# Processing and displaying the classification results\n",
    "tokens = tokenizer.convert_ids_to_tokens(inputs['input_ids'][0])\n",
    "for token, prediction in zip(tokens, predictions[0]):\n",
    "    print(f'{token}: {model.config.id2label[prediction.item()]}')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Tips for Effective Token Classification\n",
    "\n",
    "Here are some tips and best practices to enhance model performance, handle different types of data, and troubleshoot common issues in token classification tasks."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Conclusion\n",
    "\n",
    "You have now learned the fundamentals of using `{{ model_class }}` for token classification. Explore the Transformers library further to discover more advanced techniques and applications."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Fine-Tuning the Model for Token Classification\n",
    "\n",
    "In this section, we'll fine-tune `{{ model_class }}` for a specific token classification task. Fine-tuning involves adapting the pre-trained model to a particular dataset to improve its performance on that specific task. We'll choose an appropriate dataset for our token classification task."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Preparing the Dataset\n",
    "\n",
    "We'll start by preparing our dataset for the token classification task. For example, we can use a dataset for named entity recognition."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Load and prepare a token classification dataset\n",
    "from datasets import load_dataset\n",
    "dataset = load_dataset('token_classification_dataset_name')\n",
    "\n",
    "# Preprocessing function for the dataset\n",
    "def preprocess_function(examples):\n",
    "    # Tokenize the sentences and align the labels\n",
    "    # Note: Adjust the tokenization and label alignment as per your dataset\n",
    "    return tokenizer(examples['sentences'], 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, let's set up the optimizer and other training parameters."
   ]
  },
  {
   "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 based on your GPU's capacity"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Training Loop\n",
    "\n",
    "Now, let's implement the training loop for fine-tuning the model on our dataset."
   ]
  },
  {
   "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, it's important to evaluate the model on a validation set to assess its performance."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "model.eval()\n",
    "# Add evaluation code to assess the model's performance"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Conclusion\n",
    "\n",
    "This tutorial covered the process of fine-tuning `{{ model_class }}` for a token classification task. Experiment with different datasets, hyperparameters, and training strategies to optimize the model for your specific requirements."
   ]
  }
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