  Topic Modeling refers to the problem of discovering the main topics that have
occurred in corpora of textual data, with solutions finding crucial
applications in numerous fields. In this work, inspired by the recent
advancements in the Natural Language Processing domain, we introduce FAME, an
open-source framework enabling an efficient mechanism of extracting and
incorporating textual features and utilizing them in discovering topics and
clustering text documents that are semantically similar in a corpus. These
features range from traditional approaches (e.g., frequency-based) to the most
recent auto-encoding embeddings from transformer-based language models such as
BERT model family. To demonstrate the effectiveness of this library, we
conducted experiments on the well-known News-Group dataset. The library is
available online.
