In the exploration of synthetic text quality metrics, the blog post introduces an innovation built on the Fréchet BERT Distance (FBD) that aligns with their specific use case, improving upon previous methods by not requiring one-to-one real and synthetic text pairings. The post discusses challenges with existing metrics like BLEU and ROUGE which fail to capture semantic similarities in text with minimal overlap, and highlights newer approaches like BERTScore and BLEURT that use embeddings but still have limitations due to their need for matched pairs. The Fréchet BERT Distance, inspired by image evaluation metrics, compares distributions of real and synthetic text embeddings, offering a quick and effective measure without requiring exact text matches. Additionally, Sentence Transformers (SBert) are employed to enhance the semantic meaning of sentence embeddings, which then inform the Fréchet Cosine Similarity Distance (FCSD) metric. Experimental results indicate that using SBert's `nli-roberta-base-v2` model with FBD outperforms traditional metrics, though FCSD's performance was less consistent. Overall, the blog post presents these innovations as a significant advancement for evaluating synthetic text, with potential applications in evaluating language models in future studies.