Sally is a small tool for mapping a set of strings to a set of vectors. This mapping is referred to as embedding and allows for applying techniques of machine learning and data mining for analysis of string data. Sally can be applied to several types of strings, such as text documents, DNA sequences or log files, where it can handle common formats such as directories, archives and text files of string data. Sally implements a standard technique for mapping strings to a vector space that is often referred to as vector space model or bag-of-words model. The strings are characterized by a set of features, where each feature is associated with one dimension of the vector space. The following types of features are supported by Sally: bytes, words, n-grams of bytes and n-grams of words. Sally proceeds by counting the occurrences of the specified features in each string and generating a sparse vector of count values. Alternatively, binary or TF-IDF values can be computed and stored in the vectors. Sally then normalizes the vector, for example using the L1 or L2 norm, and outputs it in a specified format, such as plain text or in LibSVM or Matlab format. The following technical articles detail the background of the embeddeding implemented in Sally, starting with the design and extraction of string features and reaching over to computation of distance and kernel functions for strings Sally: A Tool for Embedding Strings in Vector Spaces Konrad Rieck, Christian Wressnegger, and Alexander Bikadorov. Journal of Machine Learning Research (JMLR), 13(Nov):3247-3251, 2012.