Having the framework handed to you sounds like a relief until you realize the tokenizer is actually the part that needs to work correctly for everything downstream.
Start by clarifying the tokenization strategy (e.g., word-level, subword like BPE) and the expected vocabulary size. Then implement a Tokenizer class with a vocabulary dictionary and methods for encoding (word-to-token) and decoding (token-to-word), handling unknown tokens and special tokens. Test with sample text to ensure round-trip consistency.
Pro tip: Mention that in production, tokenizers are often pre-trained and reused; here, focus on clean, efficient code with proper handling of edge cases like OOV and padding, which shows you think beyond the notebook.
Ask about the tokenization granularity (word, character, subword), vocabulary size, and whether special tokens (e.g., <unk>, <pad>) are needed. Confirm if the tokenizer should be trained on the given corpus or if a pre-built vocabulary is provided.
Outline a class with a vocabulary mapping (token to ID and ID to token), and methods for encoding and decoding. Consider adding a method to build the vocabulary from a corpus if needed.
Write a function that splits input text into tokens (e.g., by whitespace or using regex), maps each token to its ID using the vocabulary, and handles unknown tokens by assigning the <unk> ID.
Write a function that takes a list of token IDs, maps each ID back to its token using the inverse vocabulary, and joins them into a string, ensuring special tokens are handled appropriately.
Run a few examples to verify that encoding then decoding returns the original text (or a close approximation), and check edge cases like empty strings, unknown words, and special tokens.
AI-generated suggestions, not part of the candidate's original notes. May be inaccurate — verify before relying on them.
Start by clarifying the problem setup (e.g., model architecture, dataset, task) and then implement a standard optimizer (e.g., Adam) and loss function (e.g., cross-entropy) appropriate for the task. Write a minimal training loop that logs loss per iteration and verify that the loss decreases over time, discussing potential issues like learning rate tuning or overfitting.
Pro tip: Mention that you would also monitor validation loss and use learning rate scheduling to ensure robust convergence, showing awareness of practical training dynamics beyond just the training loss.
Ask about the model, data, and task to choose an appropriate optimizer and loss function. For example, for classification, use cross-entropy loss and Adam optimizer.
Write code to instantiate the loss criterion and optimizer, ensuring they are compatible with the model parameters. For instance, use nn.CrossEntropyLoss and torch.optim.Adam.
Create a loop that iterates over the dataset, performs forward pass, computes loss, backpropagates, and updates weights. Log the loss at each iteration or epoch.
Run the loop and observe the loss trend. If loss doesn't decrease, check learning rate, data preprocessing, or model initialization. Plot loss over iterations for clarity.
Talk about optimizer choices (SGD vs Adam), loss function alternatives, and techniques like learning rate scheduling or regularization to improve convergence.
AI-generated suggestions, not part of the candidate's original notes. May be inaccurate — verify before relying on them.