Attention Mechanism is a technique that enables the model to focus on different parts of the input sequence when making predictions. It allows the model to weigh the importance of various words or tokens in a sentence relative to each other, which is crucial for understanding context and relationships between words.

Here’s a breakdown of how it works:

1. Contextual Focus: Instead of processing input tokens in a fixed, sequential manner, the attention mechanism evaluates the relevance of each token in relation to others. This helps the model to "attend" to the most relevant parts of the input for each prediction.

2. Weighting: The mechanism assigns a weight to each token in the input sequence based on its importance to the current prediction or context. These weights are computed dynamically and help the model focus on tokens that are more significant for generating the output.

3. Attention Scores: The weights are derived from attention scores, which are calculated using a function (often a dot product or a learned scoring function) that measures how well the tokens align with each other.

4. Aggregation: The weighted information from the input tokens is aggregated to form a context vector, which is used to make predictions or generate the output sequence.