vizseq: A Python repository from jagsapphub

VizSeq is a Python toolkit for visual analysis on text generation tasks like machine translation, summarization, image captioning, speech translation and video description. It takes multi-modal sources, text references as well as text predictions as inputs, and analyzes them visually in Jupyter Notebook or a built-in Web App (the former has Fairseq integration). VizSeq also provides a collection of multi-process scorers as a normal Python package.

[Paper] [Documentation] [Blog]

Task Coverage

Source	Example Tasks
Text	Machine translation, text summarization, dialog generation, grammatical error correction, open-domain question answering
Image	Image captioning, image question answering, optical character recognition
Audio	Speech recognition, speech translation
Video	Video description
Multimodal	Multimodal machine translation

Metric Coverage

Accelerated with multi-processing/multi-threading.

Type	Metrics
N-gram-based	BLEU (Papineni et al., 2002), NIST (Doddington, 2002), METEOR (Banerjee et al., 2005), TER (Snover et al., 2006), RIBES (Isozaki et al., 2010), chrF (Popović et al., 2015), GLEU (Wu et al., 2016), ROUGE (Lin, 2004), CIDEr (Vedantam et al., 2015), WER
Embedding-based	LASER (Artetxe and Schwenk, 2018), BERTScore (Zhang et al., 2019)

Getting Started

Installation

VizSeq requires Python 3.6+ and currently runs on Unix/Linux and macOS/OS X. It will support Windows as well in the future.

You can install VizSeq from PyPI repository:

$ pip install vizseq

Or install it from source:

$ git clone https://github.com/facebookresearch/vizseq
$ cd vizseq
$ pip install -e .

Documentation

Jupyter Notebook Examples

Fairseq integration

Web App Example

Download example data:

$ git clone https://github.com/facebookresearch/vizseq
$ cd vizseq
$ bash get_example_data.sh

Launch the web server:

$ python -m vizseq.server --port 9001 --data-root ./examples/data

And then, navigate to the following URL in your web browser:

http://localhost:9001

License

VizSeq is licensed under MIT. See the LICENSE file for details.

Citation

Please cite as

@inproceedings{wang2019vizseq,
  title = {VizSeq: A Visual Analysis Toolkit for Text Generation Tasks},
  author = {Changhan Wang, Anirudh Jain, Danlu Chen, Jiatao Gu},
  booktitle = {In Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing: System Demonstrations},
  year = {2019},
}

Contact

Changhan Wang (changhan@fb.com), Jiatao Gu (jgu@fb.com)

jagsapphub/vizseq