NSP, or Next Sentence Prediction, is a pre-training task used in the training of BERT (Bidirectional Encoder Representations from Transformers) models. The NSP task is designed to enable BERT to learn not only the relationships between words within a sentence but also the relationships between sentences in a document or text corpus.
Here’s how NSP works in BERT:
1. Sentence Pair Input: BERT takes a pair of sentences as input during pre-training. These sentences are typically sampled from a large text corpus. In the input, there is a special separator token (e.g., [SEP]) between the two sentences.
2. Binary Classification: The NSP task is a binary classification task. BERT learns to predict whether the second sentence in the pair logically follows the first sentence (i.e., is the “next sentence”) or not. This task helps the model capture the relationships and cohesiveness between sentences in a document.
3. Training Objective: During training, BERT is presented with pairs of sentences, and it learns to predict whether they are connected or not. The model is trained to produce two possible labels:
— Label 1 (IsNext): If the second sentence logically follows the first sentence, the label is set to “IsNext” (1).
— Label 2 (NotNext): If the second sentence is not the next sentence, the label is set to “NotNext” (0).
4. Loss Function: BERT is trained to minimize a loss function, such as cross-entropy loss, over these binary classification labels. The goal is to make accurate predictions about whether the second sentence is a continuation of the first or not.
The NSP task is valuable because it encourages the BERT model to learn contextual understanding and discourse coherence. It enables the model to capture relationships between sentences, making it useful for a wide range of natural language understanding tasks, including question answering, sentiment analysis, and text summarization.
By training on a large corpus of text with the NSP task, BERT learns to generate contextual embeddings for words and sentences, which can then be fine-tuned for specific downstream tasks. This pre-training approach has been highly successful and has significantly advanced the state of the art in natural language processing.
