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Simple Starter Lab: Building a Text Classifier with PyTorch

Objective

The goal of this lab is to introduce you to PyTorch by building a simple AI model that classifies text.

We'll create a model that categorizes a given paragraph of text.

Prerequisites

Basic understanding of Python programming

Familiarity with neural networks

Environment Setup

Open Google Colab and create a new Python notebook.

Ensure you have PyTorch installed. If not, use:

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!pip install torch

Step 1: Import Necessary Libraries

import torch

from torch import nn

Step 2: Prepare the Dataset

For this lab, we'll use a simple paragraph of text. In real-world applications, this would be a larger dataset.

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# Example text data (feel free to change)

text_data = "PyTorch is an open source machine learning library based on the Torch library, used for applications such as computer vision and natural language processing, primarily developed by Facebook's AI Research lab."

Step 3: Data Preprocessing

Convert the text data into numerical form using a simple mapping.

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# Creating a character to index mapping

chars = list(set(text_data))

vocab_size = len(chars)

char_to_idx = {char: idx for idx, char in enumerate(chars)}

# Convert the text into numerical form

numerical_data = [char_to_idx[char] for char in text_data]

Step 4: Creating the Neural Network

Define a basic neural network with one linear layer.

class TextClassifier(nn.Module):

def __init__(self, vocab_size):

super(TextClassifier, self).__init__()

self.linear = nn.Linear(vocab_size, vocab_size)

def forward(self, x):

return torch.softmax(self.linear(x), dim=1)

model = TextClassifier(vocab_size)

**Step 5: Training the Model

As this is an introductory lab, we'll skip the training step but in a real-world scenario, you would divide your data into batches, feed it through the model, and use an optimizer and loss function to update the model weights.

Step 6: Making Predictions

For demonstration purposes, we'll make a random prediction.

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# Random input

input = torch.tensor(numerical_data).float()

# Forward pass

output = model(input)

print("Output probabilities:", output)

Lecture Instructions

Explain each step clearly, focusing on the purpose and functioning of each part of the code.

Emphasize the importance of data preprocessing in AI models.

Discuss the structure of a basic neural network, explaining the linear layer and the softmax function.

Clarify that training involves adjusting model weights based on a loss function and an optimizer, which we are not covering in detail in this starter lab.

Encourage experimentation, such as modifying the text data or the neural network structure.

Closing Notes

This lab provides a basic introduction to text processing and model building in PyTorch. Encourage your students to explore more complex datasets and neural network architectures as they become more comfortable with the basics.

The code provided in the previous response serves as a basic framework to introduce students to PyTorch in a Google Colab Notebook. However, there are some important considerations and adjustments needed to make it fully functional:

Data Preparation: The provided code uses a straightforward approach to convert characters to indices, but for a working text classification model, you might need a more sophisticated method of handling text data.

Model Input: The TextClassifier model expects input in a specific format (typically a batch of vectors). The current setup with input = torch.tensor(numerical_data).float() might not be suitable for a real use case. In practice, you would need to prepare your input data to match the expected format of the model.

Model Complexity: The TextClassifier model is extremely basic. For more realistic text classification tasks, you would likely need a more complex model, potentially using layers like nn.Embedding and recurrent layers (e.g., LSTM or GRU).

Training Loop: The provided code does not include a training loop. In a real-world scenario, you would need to implement a training loop where the model learns from the data. This involves defining a loss function, an optimizer, and iteratively updating the model's weights based on the input data.

Random Prediction: The 'prediction' step in the provided code does not represent an actual prediction task. In a typical setup, after training, you would use the model to predict the class of new, unseen data.

Software Versions: Ensure that the versions of PyTorch and other libraries in Google Colab are compatible with the code.

To summarize, while the code is a good starting point for educational purposes and to understand the basics of PyTorch and model building, it would need significant enhancements and modifications to be used for a real text classification task in Google Colab. For beginner students, focusing on the conceptual understanding and step-by-step implementation in a simplified scenario, like the one provided, can be very beneficial before moving on to more complex applications.

What does this mean and why it is done: # Creating a character to index mapping

chars = list(set(text_data))

vocab_size = len(chars)

char_to_idx = {char: idx for idx, char in enumerate(chars)}

# Convert the text into numerical form

numerical_data = [char_to_idx[char] for char in text_data]

In the provided code snippet, two key steps are performed to process the text data for use in a machine learning model. Let's break down what each part means and why it's done:

1. Creating a Character to Index Mapping

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chars = list(set(text_data))

vocab_size = len(chars)

char_to_idx = {char: idx for idx, char in enumerate(chars)}

Purpose: Machine learning models, including neural networks, cannot directly process raw text. They require the text to be converted into a numerical format. The first step in this process is creating a mapping from each unique character in the text to a unique integer.

How It's Done:

chars = list(set(text_data)): This line creates a list of unique characters (chars) present in text_data. set(text_data) removes duplicate characters, ensuring each character is represented only once. Converting this set back to a list allows us to index these characters.

vocab_size = len(chars): Here, vocab_size is calculated as the number of unique characters in the text. This represents the size of the vocabulary (i.e., all distinct elements) in the text data.

char_to_idx = {char: idx for idx, char in enumerate(chars)}: This line creates a dictionary where each unique character in chars is mapped to a unique integer (idx). The enumerate(chars) function pairs each character with a unique index (starting from 0), forming key-value pairs in the dictionary.

2. Converting Text into Numerical Form

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numerical_data = [char_to_idx[char] for char in text_data]

Purpose: Once we have the character-to-index mapping, the next step is to convert the entire text into a sequence of integers. This transformation is essential for the text to be processed by machine learning algorithms.

How It's Done:

This line iterates over each character in text_data and uses the char_to_idx dictionary to find the corresponding integer index for each character. The result is a list of integers (numerical_data), where each integer represents a character from the original text.

Why This Process is Important

Transforming text into a numerical format is a fundamental preprocessing step in most natural language processing (NLP) tasks. Neural networks and other machine learning models operate on numbers, so this transformation allows the raw text data to be input into these models for training and inference. The approach used here is a basic form of text representation, suitable for simple tasks and illustrative purposes. More complex tasks might require advanced techniques like word embeddings or tokenization at the word or subword level.

Format the code and explanations into a Jupyter Notebook format, complete with Markdown cells for explanations and Python code cells for the code. You can then copy this content into a Jupyter Notebook file.

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# Simple Text Processing and Model Building in PyTorch

This notebook introduces basic text processing and a simple neural network model using PyTorch.

## Objective

The goal is to demonstrate how to convert text data into a numerical format that can be processed by a machine learning model, and to define a basic neural network for text classification.

## Prerequisites

- Basic Python programming skills

- Understanding of neural network fundamentals

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# Importing Necessary Libraries

import torch

from torch import nn

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## Step 1: Prepare the Dataset

For this example, we use a simple paragraph of text. In a real-world scenario, this would be a larger, more complex dataset.

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# Example Text Data

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## Step 2: Data Preprocessing

We need to convert the text data into a numerical format. This involves creating a mapping from each character in the text to a unique integer.

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# Creating a Character to Index Mapping

chars = list(set(text_data))

vocab_size = len(chars)

char_to_idx = {char: idx for idx, char in enumerate(chars)}

# Convert the Text into Numerical Form

numerical_data = [char_to_idx[char] for char in text_data]

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## Step 3: Creating the Neural Network

We define a basic neural network with one linear layer for this example.

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# Defining the Text Classifier Model

class TextClassifier(nn.Module):

def __init__(self, vocab_size):

super(TextClassifier, self).__init__()

self.linear = nn.Linear(vocab_size, vocab_size)

def forward(self, x):

return torch.softmax(self.linear(x), dim=1)

model = TextClassifier(vocab_size)

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## Step 4: Making Predictions

Since this is a basic example, we demonstrate a random prediction. In a real-world application, you would train the model and then use it for predictions.

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# Making a Random Prediction

# Random Input

input = torch.tensor(numerical_data).float()

# Forward Pass

output = model(input)

print("Output probabilities:", output)

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## Conclusion

This notebook provided a basic introduction to text processing and model building in PyTorch. Remember, this is a simplified example. Real-world applications typically involve more complex data and models.

To use this in a Jupyter Notebook:

Copy each block of text or code above and paste it into a new cell in Jupyter Notebook, matching the cell type (Markdown for explanations, Code for Python code).

Run the Python code cells to execute the code.

This format provides a clear, educational structure, allowing students to follow along with both the explanations and the corresponding code.

Step 2: Implementing the Training

To resolve the `RuntimeError` and add a training loop, we need to make several changes to the code.

The error occurs because the dimensions of the input tensor and the model's linear layer aren't compatible for matrix multiplication.

Additionally, I'll implement a complete training loop for the model.

import torch

from torch import nn

import torch.optim as optim

# Define the text data

text_data = "PyTorch is an open source machine learning library."

# Create a mapping of each character to a unique index

chars = list(set(text_data))

vocab_size = len(chars)

char_to_idx = {char: idx for idx, char in enumerate(chars)}

# Convert the text into numerical form

numerical_data = [char_to_idx[char] for char in text_data]

# Function to one-hot encode the numerical data

def one_hot_encode(sequence, vocab_size):

res = torch.zeros(len(sequence), vocab_size)

for i, idx in enumerate(sequence):

res[i, idx] = 1

return res

# One-hot encode the numerical data

input_data = one_hot_encode(numerical_data, vocab_size)

# Define the text classifier model

class TextClassifier(nn.Module):

def __init__(self, vocab_size):

super(TextClassifier, self).__init__()

self.linear = nn.Linear(vocab_size, vocab_size)

def forward(self, x):

return torch.softmax(self.linear(x), dim=1)

# Instantiate the model

model = TextClassifier(vocab_size)

# Define the loss function and optimizer

loss_fn = nn.CrossEntropyLoss()

optimizer = optim.Adam(model.parameters(), lr=0.001)

# Prepare the target labels for training

# Here we're using a simple technique of predicting the next character

target = torch.tensor([char_to_idx[char] for char in text_data[1:]] + [char_to_idx[text_data[0]]])

# Training loop

for epoch in range(100):

model.train()

optimizer.zero_grad()

# Forward pass

output = model(input_data[:-1])

# Compute loss

loss = loss_fn(output, target[:-1]) # Aligning the target size to match the output size

loss.backward()

# Backward pass and optimize

optimizer.step()

if epoch % 10 == 0:

print(f'Epoch {epoch}, Loss: {loss.item()}')

# Testing the model

model.eval()

with torch.no_grad():

output = model(input_data[:-1])

predicted = torch.argmax(output, dim=1)

print('Predicted:', ''.join([chars[idx] for idx in predicted]))

### Explanation

1. **One-hot Encoding:** The text data is converted to one-hot encoded vectors. Each character is represented by a sparse vector of size equal to the vocabulary size, with a `1` in the position corresponding to the index of the character in the vocabulary and `0`s elsewhere. This representation is more suitable for processing with a neural network.

2. **Model Training:** We've added a training loop where the model learns to predict the next character in the sequence given the current character. The target for each character is the next character in the text data. Note that in a real-world scenario, text processing and target preparation would be more complex.

3. **Loss Function and Optimizer:** We use CrossEntropyLoss, suitable for classification tasks, and Adam optimizer.

4. **Evaluation:** Finally, the model is evaluated by generating a sequence of predicted characters based on the input data.

Please note that this example illustrates the key components of building the simple AI TEXT model:

text data preprocessing,

model architecture,

training,

evaluation

Let’s try running a question answering system:

from transformers import BertForQuestionAnswering, BertTokenizer import torch

# Load a pre-trained BERT model and tokenizer model = BertForQuestionAnswering.from_pretrained('bert-large-uncased-whole-word-masking-finetuned-squad') tokenizer = BertTokenizer.from_pretrained('bert-large-uncased-whole-word-masking-finetuned-squad')

# Function to ask a question def ask_question(context, question): inputs = tokenizer.encode_plus(question, context, return_tensors='pt') with torch.no_grad(): answer_start_scores, answer_end_scores = model(**inputs)

# Find the tokens with the highest `start` and `end` scores answer_start = torch.argmax(answer_start_scores) answer_end = torch.argmax(answer_end_scores) + 1

# Convert tokens to string answer = tokenizer.convert_tokens_to_string(tokenizer.convert_ids_to_tokens(inputs['input_ids'][0][answer_start:answer_end])) return answer

# Example usage context = "PyTorch is an open source machine learning library based on the Torch library." question = "What is PyTorch based on?"

answer = ask_question(context, question) print("Answer:", answer)

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