Semantic search in natural language processing aims to understand the context and intent behind queries, using advanced embedding models to provide contextually relevant results. This blog post demonstrates the use of the Amazon Multilingual Counterfactual Dataset (AMCD) and the bge-base-en-v1.5 model within the MAX Engine for counterfactual detection in customer reviews, using a binary classification task. Counterfactual statements, such as hypothetical scenarios, are identified, and the MAX Engine is used to convert text into high-dimensional vectors for semantic analysis. The post details the process of storing embeddings in a vector database, using cosine similarity for querying, and evaluating the classifier's effectiveness through accuracy, F1 score, precision, and recall. The performance of the MAX Engine is compared to PyTorch and ONNX runtime, showcasing its superior efficiency in processing batch data, with up to 2.8 times faster performance for smaller batch sizes and 1.8 times for larger ones. This highlights MAX Engine's capability to optimize resource utilization and speed in large-scale NLP tasks.