What do CrossEncoder do in SentenceTransformers

In Sentence Transformers, a CrossEncoder is a model architecture designed for tasks where you need to compare pairs of sentences or text passages to determine their relationship. It's particularly useful for tasks like:

Semantic Textual Similarity (STS): Determining how similar two sentences are in meaning.   

Re-ranking: Given a query and a list of documents, re-ordering the documents based on their relevance to the query.   

Here's a breakdown of what a CrossEncoder does and how it differs from a SentenceTransformer (bi-encoder):

Key Differences Between CrossEncoders and Bi-Encoders:

Bi-Encoders (SentenceTransformers):

Encode each sentence or text passage independently into a fixed-length vector (embedding).   

Calculate the similarity between two sentences by comparing their embeddings (e.g., using cosine similarity).

Efficient for large-scale similarity searches because you can pre-compute and store embeddings.

Cross-Encoders:

Take a pair of sentences or text passages as input and process them together.   

Produce a single output score that represents the relationship between the two inputs.   

Generally more accurate than bi-encoders for pairwise comparison tasks.   

Slower than bi-encoders because they require processing each pair of sentences individually.

How CrossEncoders Work:

Concatenation:

The two input sentences are concatenated (often with a special separator token like [SEP]).

Transformer Processing:

The concatenated input is fed into a Transformer-based model (e.g., BERT, RoBERTa).

Output Score:

The model produces a single output score, typically a value between 0 and 1, that represents the similarity or relevance between the two input sentences.   

For example, in a STS task, a score of 1 indicates high similarity, and a score of 0 indicates low similarity.

Use Cases:

Re-ranking Search Results: When you have a large set of potentially relevant documents, a cross-encoder can be used to re-rank the top-k results from a bi-encoder search, improving accuracy.   

Question Answering: Cross-encoders can be used to determine the relevance of candidate answer passages to a given question.   

Duplicate Question Detection: Identifying duplicate questions in a forum or online platform.   

Code Example (using Sentence Transformers):

from sentence_transformers import CrossEncoder

model = CrossEncoder('cross-encoder/stsb-roberta-large')

sentence_pairs = [

    ('A man is eating food.', 'A man is eating a meal.'),

    ('A man is eating food.', 'The food is being eaten by a man.'),

    ('A man is eating food.', 'A man is playing a guitar.'),

]

scores = model.predict(sentence_pairs)

for pair, score in zip(sentence_pairs, scores):

    print(f"Sentence Pair: {pair}, Score: {score}"

In summary:

CrossEncoders provide high accuracy for pairwise text comparison tasks by processing sentence pairs together, but they are computationally more expensive than bi-encoders. They are most useful when accuracy is critical and you can afford the extra processing time.