VizSeq
A Python toolkit for visual analysis and evaluation of text generation tasks like translation, summarization, and captioning.
What is VizSeq?
VizSeq is a Python toolkit for visual analysis and evaluation of text generation tasks such as machine translation, summarization, image captioning, and speech translation. It provides interactive visualizations and a comprehensive suite of metrics to help researchers and developers analyze model outputs, compare predictions against references, and identify errors efficiently. The toolkit supports multi-modal inputs and integrates with Jupyter Notebooks and a web app for flexible exploration.
Researchers and developers working on natural language generation, machine translation, summarization, or multimodal AI tasks who need to evaluate and debug model performance visually and quantitatively.
VizSeq stands out by offering a unified, visual interface for analyzing diverse text generation tasks across multiple modalities, coupled with fast, multi-process metric computation. Its integration with Fairseq and support for both notebook and web-based workflows make it a practical choice for accelerating research iteration.
Overview
An Analysis Toolkit for Natural Language Generation (Translation, Captioning, Summarization, etc.)
Use Cases
Best For
- Visualizing and comparing machine translation outputs against reference translations
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