Sign in to track progress, unlock prep layers, and browse your pipeline.Sign in
Implement n-gram model and select n
Implement an N-gram Language Model
Objective
Design and implement an n-gram language model class with specific methods for training and text generation. Additionally, discuss critical aspects of model selection and complexity trade-offs.
Class Methods
fit(file_path, n)
This method should train the language model:
- Read and tokenize a given text file, ensuring consistent tokenization.
- Build frequency counts for n-grams and (n-1)-grams.
- Compute conditional probabilities, incorporating smoothing techniques. At a minimum, implement add-k smoothing. Explain and, if possible, implement interpolated Kneser–Ney smoothing.
generate(start_tokens, max_len, sampling_strategy)
This method should generate text based on the trained model:
- Sample subsequent tokens according to the learned probabilities.
- Support multinomial sampling. Additionally, implement top-k and temperature scaling sampling strategies.
Requirements and Notes
- Tokenization: Must be consistent between training and generation. Consider handling Beginning-Of-Sentence (BOS) and End-Of-Sentence (EOS) tokens if sentence-level generation is intended.
- Smoothing: Implement at least add-k smoothing. Provide an explanation for interpolated Kneser–Ney smoothing, including relevant formulas, and if time permits, a conceptual implementation sketch.
- Sampling: Support multinomial sampling. Extend functionality to include top-k and temperature scaling sampling strategies.
- Model Selection: Discuss strategies for choosing the optimal
ngiven the dataset size and domain. Propose validation procedures (e.g., train/validation split), relevant metrics (e.g., perplexity), and techniques like regularization, backoff, or interpolation. - Complexity Analysis: Analyze the time and space complexity, as well as the memory footprint, for different values of
n.
Deliverables
- A clear description of the class design and chosen data structures.
- Pseudocode or a concise code sketch for the
fitandgeneratemethods. - Detailed explanations of smoothing methods, including formulas.
- Detailed explanations of sampling methods.
- A comprehensive strategy for selecting the optimal
n, incorporating validation and perplexity. - An analysis of the model's complexity and memory usage.
AI interview prep
Core dilemma
Balancing the expressiveness of higher-order n-grams with the inherent data sparsity and computational/memory costs, while ensuring robust probability estimation and text generation.
Mid-level signals3
Senior signals3
Red flags3
3 tradeoffs and 3 stress tests locked
Interview prep
Unlock the interviewer X-Ray rubric, tradeoff matrix, stress tests, clarifying questions, and key concepts.
Implement n-gram model and select n
Implement an N-gram Language Model
Objective
Design and implement an n-gram language model class with specific methods for training and text generation. Additionally, discuss critical aspects of model selection and complexity trade-offs.
Class Methods
fit(file_path, n)
This method should train the language model:
- Read and tokenize a given text file, ensuring consistent tokenization.
- Build frequency counts for n-grams and (n-1)-grams.
- Compute conditional probabilities, incorporating smoothing techniques. At a minimum, implement add-k smoothing. Explain and, if possible, implement interpolated Kneser–Ney smoothing.
generate(start_tokens, max_len, sampling_strategy)
This method should generate text based on the trained model:
- Sample subsequent tokens according to the learned probabilities.
- Support multinomial sampling. Additionally, implement top-k and temperature scaling sampling strategies.
Requirements and Notes
- Tokenization: Must be consistent between training and generation. Consider handling Beginning-Of-Sentence (BOS) and End-Of-Sentence (EOS) tokens if sentence-level generation is intended.
- Smoothing: Implement at least add-k smoothing. Provide an explanation for interpolated Kneser–Ney smoothing, including relevant formulas, and if time permits, a conceptual implementation sketch.
- Sampling: Support multinomial sampling. Extend functionality to include top-k and temperature scaling sampling strategies.
- Model Selection: Discuss strategies for choosing the optimal
ngiven the dataset size and domain. Propose validation procedures (e.g., train/validation split), relevant metrics (e.g., perplexity), and techniques like regularization, backoff, or interpolation. - Complexity Analysis: Analyze the time and space complexity, as well as the memory footprint, for different values of
n.
Deliverables
- A clear description of the class design and chosen data structures.
- Pseudocode or a concise code sketch for the
fitandgeneratemethods. - Detailed explanations of smoothing methods, including formulas.
- Detailed explanations of sampling methods.
- A comprehensive strategy for selecting the optimal
n, incorporating validation and perplexity. - An analysis of the model's complexity and memory usage.