In the scenario where your client's dataset is very small, containing only about 10,000 tokens, it is generally more advisable to use an n-gram language model rather than a neural language model. Here are the reasons for this recommendation:
N-gram Language Model:
Data Efficiency: N-gram language models are well-suited for small datasets because they rely on simple statistical models that estimate the probabilities of word sequences based on the frequencies of n-grams (n consecutive words) in the training data. With a limited dataset, these models can still provide reasonable language modeling results.
Lower Computational Requirements: N-gram models are computationally less intensive compared to neural language models. Training a neural language model, especially with a small dataset, may require significant computational resources and time.
Fewer Parameters: N-gram models have relatively few parameters compared to neural models, making them less prone to overfitting on small datasets. Overfitting can be a significant concern when dealing with limited data.
Ease of Implementation: N-gram models are straightforward to implement and require less specialized knowledge or resources compared to training neural models.
Neural Language Models:
Data Hunger: Neural language models, such as recurrent neural networks (RNNs) or transformer-based models, are data-hungry. They typically perform well when trained on large corpora with millions or billions of tokens. With only 10,000 tokens, neural models are likely to struggle to generalize effectively.
Risk of Overfitting: Neural language models are highly expressive and can capture complex language patterns. However, this also makes them susceptible to overfitting when trained on small datasets. They may memorize the training data rather than learning meaningful language representations.
Resource Intensive: Training neural language models, especially transformer-based models, requires significant computational resources, including powerful GPUs or TPUs. It can be impractical for small datasets due to the resource cost.
Complexity: Implementing and fine-tuning neural language models can be complex and may require expertise in deep learning and natural language processing.
In summary, given the limited size of the dataset (10,000 tokens), it is generally more practical and effective to opt for an n-gram language model. N-gram models are simpler, computationally efficient, and less prone to overfitting on small data. They can serve as a reasonable baseline for language modeling tasks on limited datasets. If the dataset size increases in the future, you can consider transitioning to more sophisticated neural language models for improved performance.