Machine Learning Training Data and Optimization

Training Data and Batching

• The training data used for the models is the standard WMT 2014 English-German dataset (4.5 million sentence pairs) for English-German and the larger WMT 2014 English-French dataset (36M sentences) for English-French.
• Byte-pair encoding was used to encode sentences, resulting in a shared vocabulary of roughly 37,000 tokens for English-German and 32,000 word-piece vocabulary for English-French.
• Sentence pairs were batched together based on approximate sentence length, with each batch containing approximately 25,000 source tokens and 25,000 target tokens.

Hardware and Schedule

• The models were trained on one machine equipped with 8 NVIDIA P100 GPUs.
• The training process for the base models took approximately 0.4 seconds per step and required 12 hours (100,000 steps) of training time.
• Larger models required 1.0 seconds per step and were trained for 3.5 days (300,000 steps).

Optimizer

• The Adam optimizer was used for training, with parameters: β1 = 0.9, β2 = 0.98, and ϵ = 10−9.
• The learning rate was dynamically adjusted during training using the formula: lrate = d−0.5model · min(step_num−0.5, step_num · warmup_steps−1.5)
• The learning rate was increased linearly for the first 4,000 training steps (warmup_steps) and then decreased proportionally to the inverse square root of the step number.

Other Training Aspects

• Beam search was used for decoding, but checkpoint averaging was not implemented.
• Experiments varying the number of attention heads, attention key and value dimensions, and other hyperparameters were conducted to evaluate model performance.
• Results indicate that increasing the number of attention heads beyond a certain point leads to reduced quality.
• Decreasing the attention key size negatively affects model quality, suggesting that more sophisticated compatibility functions may be beneficial.
• Larger models generally perform better, dropout helps prevent overfitting, and learned positional embeddings show similar results to sinusoidal positional encoding.

This quiz explores the fundamental concepts of training data, batching, and optimization techniques in machine learning. It covers topics such as dataset selection, hardware requirements, and the use of optimizers like Adam in training models. Test your knowledge on the processes involved in preparing and training a machine learning model.