VizSeq: A Visual Analysis Toolkit for Text Generation (Translation, Captioning, Summarization, etc.)
VizSeq is a Python toolkit designed to streamline visual analysis for text generation tasks such as machine translation and image captioning. Unlike existing evaluation tools that mainly focus on specific metrics and might not align with human evaluations, VizSeq offers a more unified and scalable approach. It presents a user-friendly interface, integrating the latest NLP technologies to enhance productivity through visualization features available both in Jupyter Notebook and a web app.
Users can utilize VizSeq to visualize text generation outputs, enabling them to efficiently filter, sort, and inspect examples combined with multimodal data and various metrics, all showcased in a single view. The toolkit facilitates the rapid evaluation of large datasets through multiprocess accelerated scorers that encompass a broad range of metrics including BLEU, ROUGE, and BERTScore, among others, and allows the creation of new metrics via a simple API.
This toolkit aims to spur advancements in text generation research, a field significant in numerous industrial applications, by providing a comprehensive and evolving analysis platform. The creators of VizSeq encourage contributions and feedback through GitHub as they seek to develop this into an open, unified analysis platform for the research community.
https://ai.meta.com/tools/vizseq/